Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

Dantsuji, Takao; Fukuda, Daisuke; Zheng, Nan

doi:10.1007/s11116-019-10074-y

Cited by 20 publications

(8 citation statements)

References 43 publications

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“…Moreover, Geroliminis and Daganzo [38] and Geroliminis and Sun [39][40][41][42] demonstrated empirically and through simulation experiments that well-defned MFDs exist for large-scale urban trafc networks. Given this unique feature, the MFD has been applied for neighborhood-or network-level demand and operation management, such as congestion pricing (e.g., [43,44], trafc signal control (e.g., [45,46], and road space allocation (e.g., [47,48].…”

Section: Impact Of Avs On Trafcmentioning

confidence: 99%

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low‐Speed Urban Networks

Karbasi,

Zheng,

O’Hern

2024

Journal of Advanced Transportation

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Automated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic levels. This study examines the effect of AVs on traffic flow operations and crash risks at a network scale. In addition, this study discusses the implications for designing and operating safe, low-speed urban road networks. Simulation experiments were carried out in a grid road network with AVs and human-driven vehicles operating in mixed flow conditions. To assess traffic performance, the macroscopic fundamental diagram (MFD) relationships were estimated, specifically focusing on speed-density correlations. From this analysis, it was possible to extract key traffic performance indices such as capacity and critical speed. Using time-to-collision surrogate safety measures, macroscopic safety diagrams were generated which associate the level of congestion with the potential crash conflicts among vehicles at an aggregated spatial scale. Utilizing this knowledge, a novel multiobjective optimization based on the NSGA-II algorithm was applied to identify the optimal trade-off between efficiency and safety. The presence of AVs was found to have a positive impact on the capacity, critical density, and average speed on a system level, even in low-speed scenarios. Moreover, AVs can result in increased critical density in the network, which suggests that the road system can serve more vehicles at its capacity, thus improving efficiency, while decreasing the number of conflicts. These findings are useful for both traffic planners and operators.

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Section: Impact Of Avs On Trafcmentioning

confidence: 99%

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low‐Speed Urban Networks

Karbasi,

Zheng,

O’Hern

2024

Journal of Advanced Transportation

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“…There were eight studies that applied traffic management techniques and optimisation. The transport modes included cars [46,58,60,136] and multimodal networks of bicycles, buses, cars, rail, and shared mobility systems [95], of buses and cars [157], and of buses, rail systems, and cars [125]. The decision variables included network design (corridors and/or stations' location, routes) [58,64,95,125], network modifications (to reverse road traffic flow or to leave it unaltered) [136], frequencies setting [95,125,197], fleet sizing [197], policy variables [58,125,157], modal split [64], and traffic management [46,60].…”

Section: ) Traffic Management On Congested Networkmentioning

confidence: 99%

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

Borchers,

Wittowsky,

Augusto Souza Fernandes

2024

IEEE Access

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Climate change is currently the biggest environmental threat, being the cities responsible for a significant impact on greenhouse gas emissions. In this sense, the transport sector is among the main causes for both emissions and the depletion of non-renewable resources. Considering this scenario, there is an appeal to build more accessible, smart, sustainable and energy-efficient cities, promoting energy transition in urban systems and increasing the quality of urban life. This review aims to analyse transport and urban mobility studies that employ optimisation techniques to achieve environmental, sustainability or climate change mitigation goals. After an overview regarding modelling aspects of how such goals were addressed, the nature of the objective functions, the perspectives considered, and the network application, such studies were classified into five areas and further detailed. The areas comprise: (i) planning and policy-making; (ii) environmental variables; (iii) demand and traffic management; (iv) technology and energy; and (v) nonspatial measures. In this sense, future research directions should include optimisation models that consider the social aspects of transport and the interests of passengers, operators and the community simultaneously, improve the modelling of environmental impacts to increase its robustness, and deal with large network problems.INDEX TERMS Climate change, Greenhouse gas (GHG) emissions, Optimisation, Passenger urban transport, Sustainable mobility.

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“…In another study, Song et al reported that the presence of bike-sharing has great prospects for boosting bicycle accessibility, thus, ensuring green and sustainable urban transportation [23]. Congestion pricing, travel demand management (TDM), proactive traffic prediction, and network optimizations have also proved beneficial in mitigating traffic congestion effectively [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

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Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

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Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

Cited by 20 publications

References 43 publications

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low‐Speed Urban Networks

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low‐Speed Urban Networks

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

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