Browse Topic: Urban mobility

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Research on Urban Bus Timetable Optimization Based on Deep Reinforcement Learning2026-99-05387/10/2026
Public transportation serves as a crucial component of urban mobility, contributing to the alleviation of urban congestion, reduction of travel expenses, and mitigation of air pollution. Nonetheless, the dynamic passenger demand and the complex traffic conditions render traditional bus timetables inadequate, leading to ineffective allocation of public transportation resources. Consequently, it is essential to create bus timetables that are responsive to actual traffic scenarios and fluctuating passenger demand. This study regards the bus timetable planning problem as a Markov decision-making process within a discrete time framework, proposing a deep reinforcement learning-based optimization model for bus timetables. In particular, the model is designed to account for both bus companies and passengers, incorporating a state space and reward calculation method that emphasizes passenger comfort. Then Deep Q-Network (DQN) methodology is employed to issue instructions on whether a bus departure at each time, and bus timetable is generated gradually over time. Experimental results indicate that the proposed approach significantly reduces bus travel costs and enhances the overall travel experience for passengers in comparison to traditional methods.
Xu, Jie, Xia, Dong, Yang, Jianxi, Wang, Bing
Low-Voltage Electric Vehicle with a Reconfigurable Powertrain Architecture for Urban and Extra-Urban Mobility2026-37-00256/9/2026
This paper presents a novel concept for battery electric vehicles (BEVs), referred to as the low-voltage reconfigurable electric vehicle (LVREV). The LVREV is designed to bridge the gap between L- and M-class vehicles by adopting a <60 V multi-phase powertrain combined with a swappable battery system, maintaining the overall vehicle mass below one ton. This configuration enables adaptable driving range, optimized energy consumption in urban environments, and enhanced safety. The LVREV features two distinct operating modes. Frugal mode is intended for urban use and employs a smaller battery pack to maximize efficiency and reduce vehicle mass, while Dual mode is tailored for longer extra-urban trips through the use of a dual-battery configuration. The key innovations of the LVREV concept include a reconfigurable vehicle architecture capable of meeting both urban and extra-urban mobility requirements, thus providing a highly versatile transportation solution. In addition, the low-voltage powertrain improves safety and lowers system costs, facilitating manual battery replacement and compatibility with domestic charging infrastructure. By integrating these technological solutions, the LVREV expands the potential of low-voltage electric vehicles and supports the development of more flexible, efficient, and user-oriented mobility concepts. Experimental and simulation results demonstrate the feasibility of the proposed solution and provide initial validation of the reconfigurable powertrain and battery architecture.
Tramacere, Eugenio, Favelli, Stefano, Galluzzi, Renato, Tonoli, Andrea
An Energy-Saving Coupling-Decoupling Optimization Strategy for Electric Modular Buses Considering Vehicle to Vehicle Charging2026-99-07305/15/2026
As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs’ operations and emphasize the potential role of EMBs in supporting low-carbon and sustainable urban mobility systems.
Liao, Peng, Guo, Jiahe, Ning, Donghong, Li, Sijia, Wang, Tao
A Real-Time Spatio-Temporal Configuration Framework for Shared Autonomous Electric Vehicle (SAEV) Fleets Integrating Deep Learning and Multi-Agent Simulation2026-01-00354/7/2026
Shared Autonomous Electric Vehicles (SAEVs) can enhance urban mobility and efficiency. However, their operational performance is often hindered by the spatio-temporal imbalance between vehicle supply and passenger demand, leading to long wait times. This paper develops a novel repositioning framework where a lightweight CNN, informed by computationally intensive multi-agent simulations, enables real-time strategy deployment. The results show that: (1) An optimized repositioning policy, calibrated via multi-agent simulation, effectively cuts the mean passenger waiting time from 12.0 to 3.0 minutes (a 75% reduction). (2) A lightweight CNN surrogate model enables real-time deployment, reducing the policy computation time from ~4 hours to ~5 minutes (>98% faster). (3) The deep learning surrogate achieves this speed with a negligible performance trade-off, increasing the waiting time by only 0.156 minutes (4.9%) compared to the full optimization.
Shang, Kai, Wang, Ning
SAE TOMORROW TODAY - AI, Government, and the Future of Urban Mobility135593/13/2026
What happens when government, technology, and sustainability collide? Listen in as we down with Gabe Klein, Co-Founder of Adapt/Impact and CityFi, for a candid conversation about the future of urban mobility, the evolving role of government, and the impact of AI on our streets. From smart curb management to electrification, you'll hear real-world examples of cities embracing innovation, rethinking transportation as a utility, and navigating the economic shifts that come with change. Tune in for an inside look at how smarter infrastructure can create more connected, efficient, and greener cities. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Development of Plug-In Hybrid Motorbike and Analysis towards Sustainable Urban Mobility2026-28-01112/12/2026
The transportation system is one major catalyst to urban ecological imbalance. In developing countries, two-wheelers are considered a major mode of urban personal transportation because of their compactness, easy maneuver in heavy traffic and good fuel efficiency. In India, middle and lower middle-class people prefer to choose two wheelers, and these vehicles are dominantly fuelled by gasoline. Although, the energy consumption by a two-wheeler is comparatively less than that of a four-wheeler, they use about 60% of the nation’s petroleum for on-road vehicles and the impact on urban air quality and climatic change is significantly high. This high proportion of gasoline utilization and emission contribution by two wheelers in cities demand greater attention to improve urban air quality and near-term energy sustainability. Electrification of two-wheelers through the application of a plug-in hybrid idea is a promising solution. A plug-in hybrid motorbike was developed by putting forth a novel drive technique, which demonstrated the advantages of reducing greenhouse gas emissions and using less fuel. The experimental investigation reveals noticeable petroleum fuel savings and greenhouse emission reduction. Through the installation of a hub motor in the rear wheel, the dynamic behaviour of the prototype was examined and observed marginal changes in ride parameters. A cost-benefit analysis was also performed to estimate the payback period for the additional cost incurred.
Kannan, Prashanth, Shaik, Amjad, Talluri, Srinivasa Rao
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-01132/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless interoperability with user-facing mobile and web applications. These interfaces provide end-users with real-time visualization of parking availability, intelligent navigation to optimal spaces, and digital payment integration, thereby minimizing search time and enhancing user convenience. From an administrative perspective, the system delivers comprehensive analytics dashboards, including heatmaps of space utilization, anomaly detection for unauthorized parking, and predictive maintenance alerts for sensor nodes. Field trials conducted across a multi-level parking facility demonstrated a 32% reduction in average vehicle search time and a 21% improvement in space utilization efficiency compared to conventional systems. The end-to-end solution adheres to robust cybersecurity standards (TLS 1.2 encryption, role-based access control) and is designed for modular scalability, supporting integration with smart city infrastructure and electric vehicle charging stations. This research establishes a scalable, intelligent framework for urban parking management, contributing significantly to reduced congestion, optimized resource allocation, and enhanced urban mobility.
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
A Comprehensive Study: The Potential of Wireless Charging in Future E Mobility2026-26-01951/16/2026
Electric mobility is no longer a distant vision, it is a global imperative in the journey of fight against the climate change and the urban pollution. Yet, despite of explosive growth in the electric vehicle adoptions, a major bottleneck remains which is efficient and convenient charging. The current reliance on physical plug in charging station creates inconvenient, time consuming experience and also faces significant technical and economic challenges those threaten to stall the smooth clean transportation revolution. Without innovation in how we recharge our vehicle the promise of electric mobility appears under threat which is undermined by less efficient, less compatible, and infrastructure hurdles. Wireless charging technology stand out as the game changing breakthrough poised to tackle these all critical problems head on. By enabling the effortless, cable-free charging system across the wide spectrum of electric vehicles, from the personal cars to the public transport fleets and to the micro mobility devices, it offers a more convenient & efficient future in which powering up is as seamless as driving. Still the key challenges such as energy transfer efficiency, infrastructure investments, safety, and interoperability standards must be overcome before this technology can fulfil its transformative potential. The paper embarks on a compelling journey which start with the foundational history of wireless power & navigating through global market dynamics and emerging trends and culminating in a forensic level analysis of ten main wireless EV charging technologies. Each technology is deeply evaluated against the regressive critical criteria which including efficiency, safety, cost and scalability. Ahead a weighted multi criteria hypothesis analysis is done that predicts their future viability and application. This deep, comparative framework demystifies complex trade off and offers clear & actionable guidance for industry leaders, engineers and policymakers. The paper not only highlighting the transformative potential of wireless charging but also providing strategic insights that can reshape our urban mobility and fleet operations. As EV ecosystems evolves toward intelligence, automation and more sustainability, this research becomes indispensable not just for understanding the present but for architecting a smarter, cleaner and electrified future of transportation.
Jain, Gaurav, Premlal, P, Pathak, Rahul, Gore, Pandurang
Revolutionizing Urban Mobility in India: The Emergence of the 2-3 Wheel Combi Vehicle and the L2-5 Regulatory Framework2026-26-01871/16/2026
Growing population in Indian cities has led to packed roads. People need a quick option to commute for both personal trips and business needs. The 2-3 Wheel Combination Vehicle is a new, modular solution that switches between a two-wheeler (2W) and a three-wheeler (3W). Hero has designed SURGE S32 to be a sustainable and flexible transportation option. It is world’s first class changing vehicle. The idea is to use a single vehicle for zipping through city traffic, making deliveries, or earning an income. Manufactured to deal with the challenges of modern life, this dual-battery convertible vehicle can easily transform from a two-wheeler to a three-wheeler and vice versa within three minutes. The Surge S32 is a versatile vehicle that replaces the need for multiple specialised vehicles. By lowering the number of vehicles on the road, it decreases road congestion, reduces emissions, and improves livelihoods. It powers by electricity, ensuring sustainability in all aspects. The current Central Motor Vehicle Rules (CMVR) lacked a clause for something this new. A vehicle was either a two-wheeler or a three-wheeler, not both. You might wonder, "How do you even make rules for a vehicle that embodies dual characteristics?” For a new idea to hit the streets, you need new rules. The Ministry of Road Transport & Highways (MoRTH) under the Central Motor Vehicle Rules (CMVR) created a new L2-5 category and established a unique regulatory framework. Developed with our best safety experts and industry minds, this new framework is the foundation that allows these vehicles to exist legally and safely. It shows that India is not just building the vehicles of the future, but the rules for them too. CMVR approval framework for the L2-5 category has made it mandatory for these vehicles to comply with newly formulated standard AIS-177, which was developed carefully with pilot testing and stakeholders inputs. This category balances innovation with practical implementation. It also harmonizes taxation and insurance, while integrating seamlessly with the National Vehicle Registration Portal of India (VAHAN) system at the Regional Transport Office (RTO) level for streamlined registration and dual-configuration. The 2-3W Combi Vehicle establishes a new standard for sustainable urban mobility in India by integrating versatility, safety, and regulatory foresight.
Ali Khan, Feroz, Gupta, Eshan
Path Planning and Control Strategies for a Semi-Autonomous Light Commercial Vehicle Navigating and Avoiding Static Obstacles in Urban Environments2026-26-01341/16/2026
This paper presents the design and implementation of a Semi-Autonomous Light Commercial Vehicle (LCV) capable of following a person while performing obstacle avoidance in urban and controlled environments. The LCV leverages its onboard 360-degree view camera, RTK-GNSS, Ultrasonic sensors, and algorithms to independently navigate the environment, avoiding obstacles and maintaining a safe distance from the person it is following. The path planning algorithm described here generates a secondary lateral path originating from the primary driving path to navigate around static obstacles. A Behavior Planner is utilized to decide when to generate the path and avoid obstacles. The primary objective is to ensure safe navigation in environments where static obstacles are prevalent. The LCV's path tracking is achieved using a combination of Pure Pursuit and Proportional-Integral (PI) controllers. The Pure Pursuit controller is utilized as lateral control to follow the generated path, ensuring smooth and accurate path tracking. Additionally, a PI controller is utilized for speed control, maintaining a consistent and safe speed. Multiple tests were conducted in various urban and controlled environments, especially densely-parked city roads, ramps, residential streets to evaluate the LCV's performance. The results demonstrate the LCV's ability to safely avoid parked vehicles showing human-like decision making and motion control, also maintaining a consistent following distance with the lead-person. The solution focuses on slow-speed applications where precision is of utmost priority. Additionally, the application of ultrasonic sensors helped in achieving immediate stops in close proximity scenarios. This system has significant potential for applications in last-mile delivery, logistics, waste management, and urban mobility, offering a versatile solution for safe and efficient navigation in complex environments and narrow roads.
Ayyappan, Vimal Raj, Dhanopia, Rashmi, Ali, Ashpak, N, Ragesh, Sato, Hiromitsu
Research on Passengers’ Willingness to Travel by Rail Transit Based on Objective Factors and Subjective Perceptions2025-99-025112/23/2025
In the context of the accelerating urbanization process, the problem of urban traffic congestion has become more severe. Rail transit, with its advantages of high efficiency, convenience, and environmental friendliness, has become a key force in alleviating urban traffic pressure. An in - depth exploration of passengers’ willingness to travel by rail transit is of great significance for optimizing urban traffic planning, improving the service quality of rail transit, and promoting the sustainable development of cities. This article starts from two dimensions: objective factors and passengers’ subjective perceptions, and comprehensively uses a variety of research methods to conduct an in - depth study on passengers’ willingness to travel by rail transit. In terms of objective factors, this article analyzes the differences in subjective perceptions among different passenger groups from the perspectives of gender, age, education level, and occupation. In terms of subjective perceptions, this article deeply analyzes the impact of passengers’ perceptions of the internal value, external value, and comfort of rail transit on their travel willingness.
Wang, Gang, Huang, Lei, Yang, Yihao
Urban Mobility: A Technological Contribution to the Modeling of a Conceptual Framework in a Cyber-Physical Environment for Reducing Traffic Congestion in the Expanded Downtown Area of São Paulo2025-36-009512/18/2025
Urban mobility is one of the major challenges faced by downtown areas in cities worldwide. Understanding how to improve it is essential, as it directly impacts the quality of life of people who live and work in these regions. There is an inconsistency in the fact that vehicles are produced with high efficiency and effectiveness, yet their purpose does not align with the daily commuting needs of large city centers, especially during peak travel times. The tools used in vehicle manufacturing, such as continuous improvement, lean manufacturing, continuous flow, and the theory of constraints, have been applied to balance transportation mode options. The analyzed scenarios aim to promote sustainable development and contribute to enhancing citizens’ quality of life. This study explores the hypothesis that if the conventional unit of measurement for vehicles, typically expressed in terms of vehicle volume or flow (vehicles/hour), were replaced by a metric based on the number of people transported per square meter per hour (persons/m2/h), the resulting analyses and proposed solutions would be more effective. The research yields two main conclusions in the city of São Paulo, Brazil. First, modifying the metric through technology and conceptual modeling within a cyber-physical environment leads to a more balanced decision-making process in traffic engineering management. Second, on São Paulo’s traditional avenues (Plano de Avenidas), the number of people transported is equivalent to reducing 29.5% of cars per hour during peak hours, which in turn lowers gas emissions from motor vehicles by 64.0%, all while maintaining the same transport capacity without altering the mode of transport. This shift results in several benefits: from a social perspective, it encourages vehicle sharing, both private and collective; from an environmental perspective, it reduces pollutant emissions by promoting the use of chartered buses and ultra-compact electric shared vehicles; and from an economic perspective, it decreases travel times while enabling variable and shared pricing models for vehicles using the city’s main roads.
Mello Filho, Luiz Vicente Figueira de, Canteras, Felippe Benavente, Meyer, Yuri Alexandre, Emiliano, William Machado, Júnior, Vitor Eduardo Molina, Gabriel, João Carlos, Iano, Yuzo
Vehicle Gear Shifting Optimization for Fuel Consumption and Emissions Minimization2025-36-011112/18/2025
Vehicles powered by internal combustion engines play a crucial role in urban mobility and still represent the vast majority of vehicles produced. However, these vehicles significantly contribute to pollutant emissions and fossil fuel consumption. In response to this challenge, various technologies and strategies have been developed to reduce emissions and enhance vehicle efficiency. This paper presents the development of a solution based on optimized gear-shifting strategies aimed at minimizing fuel consumption and emissions in vehicles powered exclusively by internal combustion engines. To achieve this, a longitudinal vehicle dynamics model was developed using the MATLAB/Simulink platform. This model incorporates an engine combustion simulation based on the Advisor (Advanced Vehicle Simulator) tool, which estimates fuel consumption and emissions while considering catalyst efficiency under transient engine conditions. Based on these models, an optimization method was employed to determine the optimal gear-shifting strategy, enabling the analysis of vehicle performance, fuel consumption, and emissions over a driving cycle under different gear-shifting configurations.
Da Silva, Vitor Henrique Gomes, Carvalho, Áquila Chagas, Lopez, Gustavo Adolfo Gonzales, Casarin, Felipe Eduardo Mayer, Dedini, Franco Giuseppe, Eckert, Jony Javorski
2025 International Conference on Transportation Infrastructure and Engineering (ICTIE 2025)2025-99-030012/17/2025
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SAE TOMORROW TODAY - Solving the EV Charging Gap for City Drivers1353910/16/2025
The future of urban mobility depends on one thing: accessible EV charging infrastructure. For the 40 million U.S. drivers who park on city streets, the lack of home charging options makes transitioning to an EV nearly impossible. And that's where it's electric comes in. Unlike traditional public EV chargers, it's electric connects directly to nearby buildings and "juicy corners", offering a cost-effective, scalable solution that doesn't rely on expensive utility upgrades. Property owners benefit too -- earning passive income while enabling their communities to embrace clean, sustainable transportation. Listen in as we sit down with Nathan King, Co-Founder & CEO, and Tiya Gordon, Co-Founder & COO, discuss how it's electric is solving one of the biggest barriers to urban EV adoption by redefining how cities deploy curbside EV charging. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube.
Patterson, Lori
The VERT GPF-Retrofit Program for Cleaner Urban Mobility within the HORIZON Europe AeroSolfd Project2025-24-00959/7/2025
Launched in 2022, AeroSolfd, a HORIZON Europe project, aims to advance clean urban mobility by developing affordable and sustainable retrofit solutions for gasoline vehicles. This three-year initiative addresses not only tailpipe emissions but also brake emissions and pollution in semi-enclosed environments. Within AeroSolfd, the Swiss-based VERT association focuses on reducing tailpipe emissions using state-of-the-art Gasoline Particulate Filter (GPF) technology featuring an uncoated ceramic multicell wall-flow filter. VERT, in partnership with HJS, CPK, BFH, developed and tested a GPF-retrofit system at Technology Readiness Level 8 (TRL 8). Results demonstrate over 99% filtration efficiency for particles smaller than 500 nm on standard cycles (WLTC) and real-world driving cycles (RDE). Forty-two gasoline vehicles (GDI and PFI) were retrofitted with the GPF retrofit across Germany, Switzerland, Israel, and Denmark over a 6 to 8-month operational period. No issues were observed with filter regeneration, or increased fuel consumption, noise, drivability or secondary emissions. This paper presents the GPF retrofit program and field trial results.
Rubino, Lauretta, Mayer, Andreas C., Lutz, Thomas W., Czerwinski, Jan, Larsen, Lars C.
U-Shift IV: Innovations and Challenges in Autonomous Urban Mobility2025-01-02847/2/2025
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road operations which includes narrow streets and pedestrians. This involved integrating necessary modifications across multiple domains, including the e/e-architecture, sensor setup, software stack, and even the design of the driveboard and capsule. By utilizing systematic methods to address regulatory and safety challenges, we ensured that the vehicle met the standards required for autonomous driving in semi-public environments. In this paper, we explore the methodologies employed to achieve regulatory compliance, focusing on sensor integration, software- and e/e-architecture. We discuss our multi-modal sensor setup, which combines camera, lidar and radar to archive redundancy and enhanced environmental perception. Additionally, we provide an overview of our software architecture, emphasizing its role in ensuring safe driving functions and enabling autonomous operations.
Pohl, Eric, Scheibe, Sebastian, Münster, Marco, Osebek, Manuel, Kopp, Gerhard, Siefkes, Tjark
Reducing Traffic Congestion with Reinforcement Learning-Driven Signal Control Systems2025-01-02837/2/2025
The road network is a critical component of modern urban mobility systems, with signalized traffic intersections playing a pivotal role. Traditionally, traffic light phase timings and durations at intersections are designed by transportation engineers using historical traffic data. Some modern intersections employ trigger-based mechanisms to improve traffic flow; however, these systems often lack global awareness of traffic conditions across multiple intersections within a network. With the increasing availability of traffic data and advancements in machine learning, traffic light systems can be enhanced by modeling them as agents operating in an environment. This paper proposes a Reinforcement Learning (RL) based approach for multi-agent traffic light systems within a simulation environment. The simulation is calibrated using real-world traffic data, enabling RL agents to learn effective control strategies based on realistic scenarios. A key advantage of using a calibrated simulation is that RL agents can experiment with different control actions without compromising safety in real-world traffic. The proposed system demonstrates that RL agents can coordinate and learn optimal policies, effectively reducing overall vehicle wait times in heavy urban traffic scenarios. The reward function is carefully designed to minimize traffic congestion by reducing vehicle wait times. A comparative study between static phase timings, currently used by conventional controllers, and the RL-based policy highlights significant reductions in overall wait times. Additionally, this simulation-based approach allows RL agents to be deployed in real-time, continuously learning and adapting to live traffic data. By implementing updated control policies for traffic light phase timings, the system can effectively reduce congestion and improve traffic flow across the network.
Kalra, Vikhyat, Tulpule, Punit, Giuliani, Pio Michele
A Power Split eCVT Hybrid for Sustainable Urban Mobility2024-32-00294/18/2025
The main drivers for powertrain electrification of two-wheelers, motorcycles and ATVs are increasingly stringent emission and noise limitations as well as the upcoming demand for carbon neutrality. Two-wheeler applications face significantly different constraints, such as packaging and mass targets, limited charging infrastructure in urban areas and demanding cost targets. Battery electric two wheelers are the optimal choice for transient city driving with limited range requirements. Hybridization provides considerable advantages and extended operation limits. Beside efficiency improvement, silent and zero emission modes with solutions allowing fully electric driving, combined boosting enhances performance and transient response. In general, there are two different two-wheeler base categories for hybrid powertrains: motorcycles featuring frame-integrated internal combustion engine (ICE) and transmission units, coupled with secondary drives via chain or belt; and scooters equipped with integral single-sided swingarm power units, featuring an internal combustion engine with a continuously variable transmission (CVT). A promising hybrid scooter powertrain concept allows combining efficiency improvement with additional benefits of electric driving – utilizing a power-split electrified continuously variable transmission (e-CVT) with a planetary gearset. In this hybrid concept, the planetary gearset seamlessly manages the modulation of the transmission ratio interacting with the e-motor’s operation modes. A hybrid strategy, considering the power demand and battery state of charge was developed concurrently with the implementation of all driving modes. The paper explains the e-CVT-layout, the selection criteria of ICE and e-motor-performance, while addressing the applicable hybrid operation modes. The evaluation of performance and efficiency had been conducted in the relevant drive cycle sections.
Schoeffmann, W., Fuckar, G., Hubmann, C., Gruber, M.
Next-Gen Italian Urban Mobility: Emissions LCA and TCO Prospective for Innovative Transportation Solutions2025-01-85934/1/2025
Reducing vehicle numbers and enhancing public transport can significantly cut emissions in the transport sector. Hydrogen-fueled and battery electric buses show the potential for decarbonization, but a Life Cycle Assessment (LCA) is essential to evaluate carbon emissions from energy production and manufacturing. In addition, even associated pollutant emissions, together with components’ wear, must be taken into account to evaluate the overall environmental impact. Total Cost of Ownership (TCO) analysis complements this by assessing long-term expenses, enabling stakeholders to balance environmental and economic considerations. This study examines carbon and pollutant emissions alongside TCO for innovative urban mobility powertrains (compared with diesel), focusing on Italian current and future hydrogen and electricity mix scenarios, even considering 100 % green hydrogen (100GH), the goal being to support sustainable decision-making and to promote eco-friendly transport solutions. The results obtained reported that pushing towards hydrogen produced from renewable sources allows to drastically reduce the overall emissions from energy production for Hydrogen-Fueled Vehicles (HFVs), going even lower than Battery Electric Vehicles (BEVs) ones. On the other hand, the costs related to green hydrogen production are still too high, and it would lead to much higher opexs with respect to BEVs. Regarding pollutant emissions, HFVs allow to minimize them, while BEVs present much higher values. Despite no single vehicle concept minimizes all parameters analyzed, in the hydrogen mix (MH) scenario, HFVs might become the best option in the future due to lower hydrogen environmental impact and cost. Conversely, in the 100GH scenario, HFVs could remain financially unviable, unless green hydrogen prices drop significantly.
Brancaleoni, Pier Paolo, Damiani Ferretti, Andrea Nicolò, Corti, Enrico, Ravaglioli, Vittorio, Moro, Davide
Efficient Sidewalk Route Planning on Aerial Images with Cost-Based A* and Mini-Max Objective Function2025-01-80324/1/2025
With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for sidewalks on aerial/HD maps.
Bao, Zhibin, Lang, Haoxiang, Lin, Xianke
Letter from the Guest Editors13-06-01-00013/27/2025
Letter from the Guest Editors
Kolhe, Mohan Lal, Zhang, Ronghui
Privacy Risk Assessment of Travel Trajectories in Metro AFC Data2025-01-72142/21/2025
The rapid expansion of metro systems in major cities worldwide has resulted in the accumulation of vast amounts of travel data through Automatic Fare Collection (AFC) systems. While this data is crucial for enhancing and optimizing transportation networks, it also raises significant concerns regarding passenger privacy due to the potential exposure of individual travel patterns. In this paper, we propose a novel privacy risk assessment model aimed at quantifying the uniqueness of travel trajectories and evaluating the associated privacy threats. Utilizing AFC data from Chengdu collected in March 2021, we first employ an information entropy approach to assess the uniqueness of travel trajectories across different time granularities. We then apply the K-Means clustering algorithm to classify these trajectories into categories based on their uniqueness levels, enabling us to investigate how factors like travel time and routes influence trajectory uniqueness. To further understand the privacy implications, we simulate attacker scenarios by replicating the process of identifying users based on known travel trajectories, thereby assessing the risk of privacy exposure under various time scales. Our experimental results reveal that metro travel trajectories exhibit high uniqueness at finer time resolutions, and that travel routes significantly affect this uniqueness. Notably, even at coarser time granularities, nearly half of the users remain susceptible to identification risks. These findings highlight the critical need for effective privacy protection strategies in the management of AFC data. The insights provided by this study are essential for policymakers and transit authorities seeking to safeguard passenger privacy while leveraging AFC data for transportation improvements.
Fan, Xiaoting, Qu, Xu, Yang, Hongtai
A Real-Time Multiplayer SUMO-Unity Co-Simulation Platform for Vehicle-Aircraft Coordinated Operation2025-01-71972/21/2025
As the demands for air travel and air cargo continue to grow, airport surface operations are becoming increasingly congested, elevating the operational risks for all entities. Conventional measurement methods in airport traffic scenarios are limited by high temporal and spatial costs, uncontrollable variables, and their inabilities to account for low-probability events. Moreover, current simulation software for airport operations exhibits weak simulation capabilities and poor interactivity. To address these issues, this study developed a virtual reality traffic simulation platform for airport surface operations. The platform integrated 3D modeling technologies, including Blender and Unity, with the Photon Fusion multiplayer platform and Simulation of Urban Mobility (SUMO) traffic simulation software. By incorporating Logitech external devices, the platform enabled real-time human-driven simulations, multiplayer online interactions, and validation of airport traffic flow models. To enhance practical applicability of the platform, a scenario library for vehicle-aircraft-taxiway coordinated operations was designed based on historical data. A stated preference survey was distributed to aviation experts, evaluating scenario risk ratings and occurrence frequencies. Principal component analysis and rank sum ratio were applied to identify key scenarios, which were embedded into the platform. The results of this study simulate the interaction among vehicles, aircraft, and airport taxiways, providing a scenario-driven control strategy verification platform and real-time interactive driving decision support. This approach contributes to the digital transformation of airport surface management, enhancing operational efficiency and safety.
Zhang, Yuheng, Han, Zhongyi, Zhang, Yuhan, Ye, Zhirui
AI-Based Smart Vehicle Parking System2024-01-524212/10/2024
This project presents the development of an advanced Autonomous Mobile Robot (AMR) designed to autonomously lift and maneuver four-wheel drive vehicles into parking spaces without human intervention. By leveraging cutting-edge camera and sensor technologies, the AMR integrates LIDAR for precise distance measurements and obstacle detection, high-resolution cameras for capturing detailed images of the parking environment, and object recognition algorithms for accurately identifying and selecting available parking spaces. These integrated technologies enable the AMR to navigate complex parking lots, optimize space utilization, and provide seamless automated parking. The AMR autonomously detects free parking spaces, lifts the vehicle, and parks it with high precision, making the entire parking process autonomous and highly efficient. This project pushes the boundaries of autonomous vehicle technology, aiming to contribute significantly to smarter and more efficient urban mobility systems.
Atheef, M. Syed, Sundar, K. Sham, Kumar, P. P. Prem, Karthika, J.
Technical Paper for 11 TH SAE India International Mobility Conference Relevance of Autonomous Vehicles in the Indian Context2024-28-012212/5/2024
Autonomous vehicles (AVs) are positioned to revolutionize transportation, by eliminating human intervention through the use of advanced sensors and algorithms, offering improved safety, efficiency, and convenience. In India, where rapid urbanization and traffic congestion present unique challenges, AVs still hold a significant promise. This technical paper discusses the relevance of autonomous vehicles in the Indian context and the challenges that need to be addressed before the widespread adoption of autonomous vehicles in India. These challenges include the lack of infrastructure, concerns regarding road safety, software vulnerabilities, adaptability of change towards autonomous vehicles, and the management of traffic. The paper also highlights the government's initiatives to encourage the development and adoption of autonomous vehicles, ideology behind the legal framework and the required changes in terms of technological advancements, and urban planning. In a brief manner, this paper tends to mark up the factors impacting the economic and environmental aspects. The aim of this technical paper is to discuss about the adaptability of autonomous vehicles on Indian roads. Our vision for India incorporates autonomous vehicles (AVs) within a comprehensive mobility ecosystem. By seamlessly integrating AVs with public transportation and addressing last-mile connectivity, India has the opportunity to revolutionize its urban mobility while prioritizing inclusivity and sustainability. The paper concludes that while the adoption of autonomous vehicles in India faces significant challenges, the country's vast potential for road travel and the growing demand for sustainable transportation solutions make it a promising market for autonomous vehicles.
Mishra, Adarsh, Mathur, Gaurav
Self-Organisation Theory Based Trajectory Optimisation Method for CAVs in Diverging Area2024-01-700711/15/2024
The highway diverging area is a crucial zone for highway traffic management. This study proposes an evaluation method for traffic flow operations in the diverging area within an Intelligent and Connected Environment (ICE), where the application of Connected and Automated Vehicles (CAVs) provides essential technical support. The diverging area is first divided into three road sections, and a discrete state transition model is constructed based on the discrete dynamic traffic flow model of these sections to represent traffic flow operations in the diverging area under ICE conditions. Next, an evaluation method for the self-organization degree of traffic flow is developed using the Extended Entropy Chaos Degree (EECD) and the discrete state transition model. Utilizing this evaluation method and the Deep Q-Network (DQN) algorithm, a short-term vehicle behavior optimization method is proposed, which, when applied continuously, leads to a vehicle trajectory optimization method for the diverging area over longer periods. Simulation results using the SUMO traffic simulation platform demonstrate that the proposed EECD indicator effectively replaces the Lyapunov Exponent (LE) as a measure of chaos in the diverging area. The optimization method based on this indicator reduces the degree of chaos in the traffic flow from 2.972 to 2.685 over time, resulting in smoother and more self-organized traffic flow. Additionally, the optimization improves average speed stability for some vehicles and reduces lane-changing behavior in the diverging area compared to outcomes without the optimization method.
Fang, Zhaodong, Qian, Pinzheng, Su, Kaichun, Qian, Yu, Leng, Xiqiao, Zhang, Jian
CDO-TCN-BiGRU: An Optimized Hybrid Deep Learning Model for Shared Bicycles Demand Forecasting12-08-03-002410/22/2024
Accurate prediction of the demand for shared bicycles is not only conducive to the operation of relevant enterprises, but also conducive to improving the image of the city, facilitating people’s travel, and solving the balance between supply and demand of bicycles in the region. To precisely predict the demand of shared bicycles, a model combining temporal convolution network (TCN) and bidirectional gating recurrent unit (BiGRU) model is proposed, and the Chernobyl disaster optimizer (CDO) is used to optimize its hyperparameters. It has the ability of TCN to extract sequence features and gated recurrent unit (GRU) to mine time series data and combine the characteristics of CDO with fast convergence and high global search ability, so as to reduce the influence of model hyperparameters. This article selects the shared bicycles travel data in Washington, analyzes its multi-characteristics, and trains it as the input characteristics of the model. In the experiments, we performed comparison study and ablation study. The results show that the prediction error of the proposed model is less than other comparative models. Therefore, CDO-TCN-BiGRU model has the characteristics of high prediction precision and good stability.
Ma, Changxi, Huang, Xiaoyu, Zhao, Yongpeng, Wang, Tao, Du, Bo
Integration of AI-Powered Vehicles with Smart City Infrastructure to Transform the Future of Automotive World2024-28-002810/17/2024
Artificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing processes and enhancing efficiency. In the automotive domain, AI's adaption has ushered in a new era of innovation and driving advancements across manufacturing, safety, and user experience. By leveraging AI technologies, the automotive industry is undergoing a significant transformation that is reshaping the way vehicles are manufactured, operated, and experienced. The benefits of AI-powered vehicles are not limited to their manufacturing, operation, and enhancing the user experience but also by integrating AI-powered vehicles with smart city infrastructure can unlock much more potential of the technology and can offer numerous advantages such as enhanced safety, efficiency, growth, and sustainability. Smart cities aim to create more livable, resilient, and inclusive communities by harnessing innovation through technologies like Internet of Things (IoT), devices, data analytics, and artificial intelligence (AI) and enables data-driven decision-making to meet the evolving needs of urban populations. Integrating AI-powered vehicles with smart city infrastructure can potentially compliment it and can offer numerous advantages like: Traffic Management, Infrastructure Optimization, Parking Optimization, Safety Enhancements, Environmental Sustainability, Emergency Response, various Infrastructure, and business Investment Planning and many more. Moreover, the integration will also have positive impact on environment such as Smart city infrastructure can provide AI-powered vehicles with data on optimal routes and availability of parking slot, resulting in reduced air pollution and energy consumption. In essence, this offers a holistic approach to urban mobility, fostering safer, more efficient, and environmentally sustainable transportation systems. By leveraging advanced technologies and data-driven insights, cities can unlock new opportunities for improving quality of life, enhancing economic competitiveness, and fostering inclusive and resilient communities. In this technical manual, we delve into the futuristic implications of this integration, providing a detailed exploration of the technical aspects and benefits.
Shrimal, Harsh
EMS Optimization of a Series-Hybrid Urban Bus with Hydrogen-Fueled Engine Accounting for NOx Emissions and Eco-Driving2024-24-00099/18/2024
The need to reduce vehicle-related emissions in the great cities has led to a progressive electrification of urban mobility. For this reason, during the last decades, the powertrain adopted for urban buses has been gradually converted from conventional Internal Combustion Engine (ICE), diesel, or Compressed Natural Gas (CNG), to hybrid or pure electric. However, the complete electrification of Heavy-Duty Vehicles (HDVs) in the next years looks to be still challenging therefore, a more viable solution to decarbonize urban transport is the hybrid powertrain. In this context, the paper aims to assess, through numerical simulations, the benefits of a series hybrid-electric powertrain designed for an urban bus, in terms of energy consumption, and pollutants emissions. Particularly a Diesel engine, fueled with pure hydrogen, is considered as a range extender. The work is specifically focused on the design of the Energy Management Strategy (EMS) of the series-hybrid powertrain, by comparing the results achieved by different empirical or optimized approaches, namely Rule-Based (RB), Dynamic Programming (DP), and Pontryagin’s Minimum Principle (PMP). The simulation analyses have been carried out by a comprehensive model of the hybrid bus, that specifically accounts for performance, efficiency, and tailpipe NOx emissions of the H2 engine in a wide operating range. To this end, a model of the Selective Catalyst Reduction (SCR) system for NOx abatement, accounting for the exhaust thermal dynamics, is considered. This task is fundamental in the case of a series hybrid-electric powertrains that, depending on the EMS, may operate with long engine stops that negatively impact on SCR efficiency. The simulation analyses have been performed by considering three reference driving cycles for urban buses. In a further step, an Eco-driving (ED) algorithm was developed to optimize speed profiles, considering actual driving routes. Onboard cameras and GPS tracking devices were used to simulate Vehicle-to-Everything (V2X) data and to replicate real-world driving conditions. The full potential of Eco-driving is realized by treating the problem as a mathematical optimal control problem, with its solution derived through the application of Pontryagin's minimum principle.
Nacci, Gianluca, Cervone, Davide, Frasci, Emmanuele, LAKSHMANAN, Vinith Kumar, Sciarretta, Antonio, Arsie, Ivan
Next-gen Urban Buses: Autonomy and ConnectivityEPR20240198/26/2024
The deployment of autonomous urban buses brings with it the hope of addressing concerns associated with safety and aging drivers. However, issues related autonomous vehicle (AV) positioning and interactions with road users pose challenges to realizing these benefits. This report covers unsettled issues and potential solutions related to the operation of autonomous urban buses, including the crucial need for all-weather localization capabilities to ensure reliable navigation in diverse environmental conditions. Additionally, minimizing the gap between AVs and platforms during designated parking requires precise localization. Next-gen Urban Buses: Autonomy and Connectivity addresses the challenge of predicting the intentions of pedestrians, vehicles, and obstacles for appropriate responses, the detection of traffic police gestures to ensure compliance with traffic signals, and the optimization of traffic performance through urban platooning—including the need for advanced communication and coordination technology to maintain stability and reliability in high-traffic scenarios. Click here to access the full SAE EDGETM Research Report portfolio.
Hsu, Tsung-Ming
Toward Explainability in Urban Motion Prediction—Survey and Outlook12-08-01-00098/24/2024
With the influx of artificial intelligence (AI) models aiding the development of autonomous driving (AD), it has become increasingly important to analyze and categorize aspects of their operation. In conjunction with the high predictive power innate to AI solutions, due to the safety requirements inherent to automotive systems and the demands for transparency imposed by legislature, there is a natural demand for explainable and predictable models. In this work, we explore the various strategies that reveal the inner workings of these models at various component levels, focusing on those adapted at the modeling stage. Specifically, we highlight and review the use of explainability in state-of-the-art AI-based scenario understanding and motion prediction methods, which represent an integral part of any AD system. We break the discussion down across three key axes that are inherent to any AI solution: the data, the model architecture, and the loss optimization. For each of the axes, we outline the general methodologies for introducing explainability, and reference and review some practical realizations for each methodology. We conclude the article by identifying several strategies that we believe are yet to be fully explored, such as physics-inspired machine learning methods, neural network pretraining, graph neural networks designed using domain-specific priors, and end-to-end trainable networks based on differentiable kinematic models.
Okanovic, Ilma, Stolz, Michael, Hillbrand, Bernhard
A Multi-Target Urban Transportation Structure Model under the Optimal Capacity Limitation of Road Networks13-06-01-00038/10/2024
The construction of urban transportation infrastructures on the supply side is severely limited due to the extensive development of central urban land. Therefore, optimizing the traffic structure with limited resources is particularly important. The work used the optimum capacity of the road network as one of the constraints. Multi-objective linear programming was used to establish the traffic structure model. The total travel volume, energy consumption, travel quality, and social cost were selected as the optimization objectives of the urban transportation structure. The influencing factors of infrastructure capacity (e.g., total travel demand, optimal capacity of road network, slow traffic capacity, and parking lot capacity) were selected as the constraint conditions in optimizing urban transportation structure. The objective was to develop an optimization model considering the constraints of urban infrastructure. Finally, the optimal traffic structure was compared with the actual travel structure using the actual case of Yuexiu District, Guangzhou, China. Suggestions were provided for optimization.
Zhang, Jinwei, Gao, Jianping
Exploration of a Precise Traffic Restriction Policy on Urban River-Crossing Corridors: A Case Study in Changsha, China13-06-02-00108/8/2024
Many cities are built around rivers in the world, and the river-crossing corridors are often their traffic bottlenecks, leading to severe congestions. Changsha is a city divided into two parts by a river with eight river-crossing corridors in China. Aiming at this issue, take Changsha as an example, this study explores developing a precise traffic restriction policy on those river-crossing corridors. First, an investigation is conducted to collect traffic flow data of those corridors. It is found that those corridors generally have serious congestion at peak hours, but their congestion levels vary greatly by corridor and direction. Then, two Greenberg models are developed for the 4-lane and 6 & 8-lane corridors, respectively, to figure out their traffic flow features. Third, a precise traffic restriction policy that balances traffic flows in different corridors is proposed. It would restrict 10% of motor vehicles on those most congested corridors, and the restricted vehicles are proportionally diverted to the neighboring non-congested corridors by detour distances. Finally, based on the estimated Greenberg models, traffic speeds of those corridors after traffic restrictions are then predicted. It is found that traffic congestions in those congested corridors are greatly alleviated, and the average travel speed of all the corridors increases by 2.8 km/h at the AM peak and 4.5 km/h at the PM peak, respectively.
Liu, Chenhui, Luo, Qiuju, Wang, Xingyu
Exploration of the Impact of Built Environment Factors on Morning and Evening Peak Ridership at Urban Rail Transit Stations: A Case Study of Changsha, China13-06-02-00097/22/2024
To identify the influences of various built environment factors on ridership at urban rail transit stations, a case study was conducted on the Changsha Metro. First, spatial and temporal distributions of the station-level AM peak and PM peak boarding ridership are analyzed. The Moran’s I test indicates that both of them show significant spatial correlations. Then, the pedestrian catchment area of each metro station is delineated using the Thiessen polygon method with an 800-m radius. The built environment factors within each pedestrian catchment area, involving population and employment, land use, accessibility, and station attributes, are collected. Finally, the mixed geographically weighted regression models are constructed to quantitatively identify the effects of these built environment factors on the AM and PM peak ridership, respectively. The estimation results indicate that population density and employment density have significant but opposite influences on the AM and PM peak ridership, which can be attributed to the opposite travel directions of commuters in the AM and PM peak. The recreational facility density, road density, and 10-min walking accessibility could significantly positively affect the PM peak ridership, and their influences vary greatly over space. Besides, the operating time of stations significantly positively affects both the AM and PM peak ridership, transfer stations have significantly larger PM peak ridership and terminal stations have significantly larger AM peak ridership. The findings are expected to provide new insights for agencies to formulate appropriate measures to improve the ridership of urban rail transit.
Su, Meiling, Liu, Ling, Chen, Xiyang, Long, Rongxian, Liu, Chenhui
Toward Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations12-08-01-00037/12/2024
Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the qualitative explainable graph (QXG), which is a unified symbolic and qualitative representation for scene understanding in urban mobility. The QXG enables interpreting an automated vehicle’s environment using sensor data and machine learning models. It utilizes spatiotemporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an interpretable scene model. A QXG can be incrementally constructed in real-time, making it a versatile tool for in-vehicle explanations across various sensor types. Our research showcases the potential of QXG, particularly in the context of automated driving, where it can rationalize decisions by linking the graph with observed actions. These explanations can serve diverse purposes, from informing passengers and alerting vulnerable road users to enabling post hoc analysis of prior behaviors.
Belmecheri, Nassim, Gotlieb, Arnaud, Lazaar, Nadjib, Spieker, Helge
Effects of Renewable Fuels on the Performance and Emissions of a Small Displacement Diesel Engine for Urban Mobility2024-37-00196/12/2024
In the frame of growing concerns over climate change and health, renewable fuels can make an important contribution to decarbonizing the transport sector. The current work presents the results of an investigation into the impact of renewable fuels on the combustion and emissions of a turbocharged compression-ignition internal combustion engine. An experimental study was undertaken and the engine settings were not modified to account for the fuel's chemical and physical properties, to analyze the performance of the fuel as a potential drop-in alternative fuel. Three fuels were tested: mineral diesel, a blend of it with waste cooking oil biodiesel and a hydrogenated diesel. The analysis of the emissions at engine exhaust highlights that hydrogenated fuel is cleaner, reducing CO, total hydrocarbon emissions, particulate matter and NOx.
Chiavola, Ornella, Matijošius, Jonas, Palmieri, Fulvio, Recco, Erasmo
Transportation Ecosystem and Autonomy: Vol. 1EPRCOMPV1920235/3/2024
One-way car-sharing services (CSSs) are believed to be a promising transportation mode for urban mobility. Because of the disparity of city functional areas and population, travel demand and vehicle supply in a CSS system may inevitably tend to be imbalanced as well. Therefore, an essential requirement of one-way CSSs is the capability of providing fleet management solutions to improve the quality of service and system performance. In other words, a CSS depends heavily on technologies that offer strategic decisions for topics like fleet sizing, location and capacity of depots and charging stations, matching of travelers with vehicles, relocation of vehicles and dispatchers for fleet rebalancing, and the balancing and charging schedules of electric vehicles. This chapter addresses trending CSS technologies and outlines some insights into the existing unsettled issues and potential solutions. The discussions and outlook are presented as a collection of key points encountered in system planning, configuration, and especially fleet operation. In doing so, the focus is on innovation in technologies, policies, operations, and regulations that impact operators, users, and transport management authorities.
Guo, Ge, Hou, Yuqin, Kang, Ming, Lv, Chen, Hemphill, Jeff
A Traffic Microsimulation Based Study of an Electric Road System with Dynamic Wireless Power Transfer2024-26-01021/16/2024
Electrification of road transport is a critical step towards establishment of a sustainable transport ecosystem. However, a major hindrance to electric mobility is the high cost and weight of the battery pack. Downsizing the battery pack will not only address these issues, but will also reduce embedded emissions due to battery manufacturing. One approach towards reducing battery pack size and still offering the user of electric vehicles similar mobility experiences as in case of conventional vehicles is to set up extensive network of charging or battery swapping stations. Another approach is to provide the vehicle with required energy while it is on the move. However, conventional systems such as overhead line or conducting rails have several disadvantages in the urban environment. One solution that has come up in this regard in recent times is the concept of Electric Roads System (ERS), which involves dynamic wireless power transfer (DWPT) to the vehicles from power transmitters embedded in the road they are driving on. The vehicles using ERS can have a downsized battery pack to enable it run on segments without ERS. Major advantage of the ERS are reductions in the cost of the battery pack and charging time. The vehicles also have reduced energy consumption due to reduction in mass. In this paper, an ERS is simulated using traffic microsimulation tool Simulation of Urban Mobility (SUMO) with heterogenous traffic. Effect of vehicle speed on charging of battery is considered. Based on the simulation results, impacts of such a system on the traffic flow and the electric power supply system are studied. The variation of the state of charge of the battery pack of the vehicles is also studied.
Sardar, Arghya, Prasad, Mukti
Vehicle Trajectory Prediction in Highway Merging Area Using Interactive Graph Attention Mechanism2023-01-711012/31/2023
Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism. Additionally, GAT are utilized to model the interactions between traffic agents and the map. The method we propose comprehensively incorporates the influences of time, space, and the map on trajectories. The interactive graph models can serve as effective prior knowledge for learning-based approaches, thereby enhancing the acquisition of interaction patterns among traffic scenarios and facilitating the interpretability of the method. We evaluate the performances of our method using real-world trajectory datasets from the highway merging area, i.e., the Exits and Entries Drone Dataset (exiD). Comparative analysis against classical algorithms demonstrates a reduced trajectory prediction error for prediction horizons of both 3s and 4s.
Wu, Xigang, Chu, Duanfeng, Deng, Zejian, Xin, Guipeng, Liu, Hongxiang, Lu, Liping
EVTOL Flight Control System Safety: An Example of Application Using MBSA2023-01-710512/31/2023
Electrical Vertical Takeoff and Landing (eVTOL) vehicles hold great promises for revolutionizing urban mobility. Their emergences as a transformative transportation technology has led multiple Original Equipment Manufacturers (OEM) competing for market share, with important variety of technical solutions, all necessitating to demonstrate the compliance to safety requirements and regulations. Model Based Safety Analysis (MBSA), newly introduced in ARP4761A and based on compositional and modular representation of failure propagation paths within one system, provides a unique opportunity to increase efficiency by maximizing the possible reuse of safety analyses elements across multiple architectures (“product line” philosophy). Generic library of safety models for elements of variant architectures can be efficiently constructed using MBSA techniques that can then support safety analyses on variant architectures or architectures trade-off. This approach can facilitate a safety process that enable customized safety solutions without complete re-engineering of the safety analyses for each architecture. The purpose of this paper is to present and illustrate one work performed on the definition of a safe Flight Control System for eVTOL, leveraging the capacity of a MBSA based approach to ensure high level of agility and rapid responsiveness. The first sections will present the need, the MBSA approach and a general modelling process that can be used to employ MBSA methodology. Then, an example of eVTOL Flight Control System architecture and safety analyses will be detailed to picture how MBSA, coupled with a generic component library, can provide an easily adaptable safety solution. Finally, we discuss some possible next steps and future work identified in order to certify a solution thanks to this method.
Adeline, Romain, Wang, Jia, Hua, Angelina
Theoretical Analysis of Multi-Zone and Transported Probability Density Function Approaches Applied to Low Temperature Combustion Process2023-24-00608/28/2023
Electrification of transport, together with the decarbonization of energy production are suggested by the European Union for the future quality of air. However, in the medium period, propulsion systems will continue to dominate urban mobility, making mandatory the retrofitting of thermal engines by applying combustion modes able to reduce NOx and PM emissions while maintaining engine performances. Low Temperature Combustion (LTC) is an attractive process to meet this target. This mode relies on premixed mixture and fuel lean in-cylinder charge whatever the fuel type: from conventional through alternative fuels with a minimum carbon footprint. This combustion mode has been subject of numerous modelling approaches in the engine research community. This study provides a theoretical comparative analysis between multi-zone (MZ) and Transported probability density function (TPDF) models applied to LTC combustion process. The generic thermo-kinetic balances for both approaches have been analyzed in term of similarities. Only onion-skin for MZ models have been considered in this study. The governing assumptions linked to sub-models for each approach to describe mixing process for TPDF and interzonal heat and mass transport for MZ are discussed. This step identifies the calibrated model parameters for each approach and their effects on the accuracy in predicting LTC mode simulations. This work shows that the transported probability density function model has fewer parameters to calibrate compared to multi-zone model. Transported probability density function seems easier to use for LTC process.
Maroteaux, Fadila, Mancaruso, Ezio, Pommier, Pierre-Lin, Vaglieco, Bianca Maria
A Comparison of Methanol, Methane and Hydrogen Fuels for SI Engines: Performance and Pollutant Emissions2023-24-00378/28/2023
The urban mobility electrification has been proposed as the main solution to the vehicle emission issues in the next years. However, internal combustion engines have still great potential to decarbonize the transport sector through the use of low/zero-carbon fuels. Alcohols such us methanol, have long been considered attractive alternative fuels for spark ignition engines. They have properties similar to those of gasoline, are easy to transport and store. Recently, great attention has been devoted to gaseous fuels that can be used in existing engine after minor modification allowing to drastically reduce the pollutant emissions. In this regard, this study tries to provide an overview on the use of alternative fuels, both liquid and gaseous in spark ignition engines, highlighting the benefits as well as the criticalities. The investigation was carried out on a small displacement spark ignition engine capable to operate both in port fuel and direct injection mode. Engine was fueled with gasoline and methanol in port mode to exploit the advantages of this technology for liquid fuels. Gaseous fuels were injected directly in the chamber to prevent the drawbacks of power loss and abnormal combustion. Tests were performed at different operating conditions typical of urban and extra-urban patterns. Combustion behavior of the tested fuels was analyzed through indicated data. Gaseous fuels were measured at raw exhaust. Particles were characterized in terms of number and size at diluted exhaust. In general, it was found out a benefit in terms of pollutant emissions with alternative fuels compared to gasoline. The interesting result regards the particle emissions that depend on the combination of the fuel characteristics and the operating conditions. In particular, at some test points, hydrogen shows high particle emissions with values comparable to those of other tested fuels highlighting the contribution of lubricating oil that plays a more significant role when low/zero carbon gaseous fuels are used.
Catapano, Francesco, Di Iorio, Silvana, Magno, Agnese, Sementa, Paolo, Vaglieco, Bianca Maria
HORIZON Europe Project AeroSolfd: GPF-Retrofit for Cleaner Urban Mobility2023-24-01148/28/2023
Ultrafine particles, in particular solid sub-100 nm particles pose high risks to human health due to their high lung deposition efficiency, translocation to all organs including the brain and their harmful chemical composition; due to dense traffic, the population in urban environments is exposed to high concentrations of those toxic air contaminants, despite these facts, they are still widely neglected. Therefore, the EU-Commission set up a program for clean and competitive solutions for different problem areas which are regarded to be hotspots of such particles. HORIZON AeroSolfd is an EU project, co-funded by Switzerland that will deliver affordable, adaptable, and sustainable retrofit solutions to reduce exhaust tailpipe emissions from petrol engines, brake emissions and pollution in semi-closed environments. VERT, a Swiss based international industry organization, has a long research history in the field of nanoparticle filtration and it is in charge of reducing tailpipe emissions of gasoline vehicles by using the best available retrofit filtration technology (BAT). VERT will apply the newest high-efficient GPF technology in three high mileage fleets, in Germany, Switzerland and Israel. The project will also serve as a platform to continue research on PN emissions as well as on secondary emissions from GDI and PFI petrol engines. In addition, the “high emitter phenomena” will be further analysed with a NPTI testing campaign of 1000 gasoline vehicles, including GDI, PFI and GPF equipped vehicles.
Rubino, Lauretta, Mayer, Andreas, Czerwinski, Jan, Lutz, Thomas, Larsen, Lars, Engelmann, Danilo, Lehmann, Martin
Intersection Signal Control Based on Speed Guidance and Reinforcement Learning2023-01-07214/11/2023
As a crucial part of the intelligent transportation system, traffic signal control will realize the boundary control of the traffic area, it will also lead to delays and excessive fuel consumption when the vehicle is driving at the intersection. To tackle this challenge, this research provides an optimized control framework based on reinforcement learning method and speed guidance strategy for the connected vehicle network. Prior to entering an intersection, vehicles are focused on in a specific speed guidance area, and important factors like uniform speed, acceleration, deceleration, and parking are optimized. Conclusion, derived from deep reinforcement learning algorithm, the summation of the length of the vehicle’s queue in front of the signal light and the sum of the number of brakes are used as the reward function, and the vehicle information at the intersection is collected in real time through the road detector on the road network. Finally, the proposed method is implemented through the SUMO (Simulation of Urban Mobility) simulation platform. The results demonstrate the effectiveness of the proposed model by obtaining the space-time trajectory map of the vehicle before and after optimization, as well as the vehicle’s travel time and fuel consumption.
Lu, Gaohui, Zhan, Zhenfei, Rehman, Hamza, Chen, Xiatong, He, Xin
An Approach to Model a Traffic Environment by Addressing Sparsity in Vehicle Count Data2023-01-08544/11/2023
For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate. This study will primarily assist in traffic modeling for Fuel Efficiency (FE) of individual Connected and Autonomous Vehicles (CAV) estimation (road safety and fleet-wide efficiency are out of the scope). We propose a process to identify typical characteristics of trips that are most critical for CAV’s FE from single-vehicle data. We then use this data along with vehicle counts to calibrate the traffic model such that the drive cycle characteristics of the Vehicle Under Test (VUT) are matched with the data collected from the real test. This calibration procedure ensures that the vehicle in the simulation environment observes speed profiles that allow realistic FE estimates. In this paper, the traffic modeling calibration is performed in Simulation of Urban MObility (SUMO) where we demonstrate the approach for the Columbus, OH metropolitan area. The available data is in the form of edge-based traffic count commonly known as Annual Average Daily Traffic (AADT), provided by the Ohio Department of Transportation (ODOT).
Patil, Mayur, Tulpule, Punit, Midlam-Mohler, Shawn
The LCA Analysis Applied to Urban Mobility2022-24-00229/16/2022
Electric propulsion is the object of intense research efforts all over the world, as a viable solution to fossil resource exploitation and pollutant emissions, towards a sustainable development. In this paper, we perform a thorough Life Cycle Assessment (LCA) of multiple electrical solutions for urban mobility, from bicycles to buses, comparing the results to those of traditional, fossil-fuel-based vehicles. This activity is of particular interest as the decision of European Parliament to interrupt the fossil fuel vehicles starting from 2035. To assess the life-cycle impact of each solution, several routes within middle size Italian cities, representative of the most Italian cities have been considered. This analysis has been performed by means of an ad-hoc integrated procedure with on-line, free tools that account also for traffic distribution. To carry out a complete study, an LCA analysis has been done which includes all life’s phase of the vehicles, starting from production to disposal.
Andreassi, Luca, De Angelis, Lorenzo
Car-sharing Mobility-on-Demand SystemsEPR20220188/24/2022
One-way car-sharing services (CSSs) are believed to be a promising transportation mode for urban mobility. Due to the disparity of city functional areas and population, travel demand and vehicle supply in a CSS may inevitably tend to be imbalanced as well. Therefore, an essential requirement of one-way CSSs is the capability of providing fleet management solutions to improve quality of service and system performance. In other words, a CSS depends heavily on technologies that offer strategic decisions on topics like Fleet sizing Location and capacity of depots and charging stations Matching of travelers with vehicles Relocation of vehicles and dispatchers for fleet rebalancing Balancing and charging schedules of electric vehicles Car-sharing Mobility-on-Demand Systems addresses trending CSS technologies and outlines some insights into the existing unsettled issues and potential solutions. The discussions and outlook are presented as a collection of key points encountered in system planning, configuration, and especially fleet operation. In doing so, the focus is on innovation in technologies, policies, operations, and regulations that impact operators, users, and transport management authorities. Click here to access the full SAE EDGETM Research Report portfolio.
Guo, Ge, Hou, Yuqin, Kang, Ming
A Hybrid Sensor-Fusion System to Locate the Electric Gridlines by UAV for Range Extension in Urban Areas2022-26-00075/26/2022
This paper explores the efficacy and efficiency of a system for the effective location of electric gridlines during daytime and night-time by the onboard and offboard transceivers of UAV through vehicle to infrastructure communication. The usage of electric gridlines in urban areas helps to extend the range of the UAVs by charging the onboard battery using an extended arm. The same arm can also be used for direct propulsion of the motors onboard UAV, thereby minimizing the reliance on battery. UAVs with advanced Image processing algorithms are utilized in the inspection of the electric grid lines themselves in the Power industry. The camera based algorithms are not effective during night-time when the gridlines are near invisible. This can be mitigated by evaluating light in other spectral ranges, but this would add to the load of the UAV. We propose a system which combines multiple information sources and helps locate the gridlines for range extension, specifically for the delivery of packages in the Urban Mobility domain. The system utilizes annotated maps for locating any electric grid lines in the vicinity. The finer control needed for placing the extension arm on live electric wire is done using a set of three radio transceivers installed on an electric pole and a double or triple transceiver configuration onboard UAV which locates the live-wire through deductive analysis of sensor data. The trajectory planning subsystem can utilize this information for establishing an efficient route and make multiple deliveries.
Pappala, Lokendra Pavan Kumar, Enagandula, Srujan, Manoharan, Sandeepkumar
Co-Simulation Platform for Modeling and Evaluating Connected and Automated Vehicles and Human Behavior in Mixed Traffic12-05-04-00254/21/2022
Modeling, prediction, and evaluation of personalized driving behaviors are crucial to emerging advanced driver-assistance systems (ADAS) that require a large amount of customized driving data. However, collecting such type of data from the real world could be very costly and sometimes unrealistic. To address this need, several high-definition game engine-based simulators have been developed. Furthermore, the computational load for cooperative automated driving systems (CADS) with a decent size may be much beyond the capability of a standalone (edge) computer. To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage. To evaluate this platform, we select cooperative on-ramp merging in mixed traffic as a study case, and establish human-in-the-loop (HuiL) simulations. The results show that our proposed platform can facilitate data collection and performance assessment for modeling personalized behaviors and interactions in CADS under various traffic scenarios.
Zhao, Xuanpeng, Liao, Xishun, Wang, Ziran, Wu, Guoyuan, Barth, Matthew, Han, Kyungtae, Tiwari, Prashant
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