The positive impacts of designing transition between traffic signal plans considering social cost

Peñabaena‐Niebles, Rita; Cantillo, Víctor; Moura, José Luis

doi:10.1016/j.tranpol.2019.05.020

Cited by 10 publications

(8 citation statements)

References 18 publications

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“…In our current approach we are only aiming to reduce the traffic density, minimizing the time needed to travel between two points in the city and therefore the pollution. In future work we would like to broaden the scope of our system by exploring the addition of different social objectives (Peñabaena-Niebles et al, 2020) such as taking into account pedestrians or public transport. We are also interested on analysing how the different parameters such as the monitoring and learning rates affect the system's performance.…”

Section: Related Workmentioning

confidence: 99%

On‐line modelling and planning for urban traffic control

2021

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Urban Traffic Control is a key problem for most big cities. Current approaches to handle the city traffic rely on controlling traffic lights. The systems in operation range from static control of traffic light phases to adaptive systems based on numeric models and traffic sensors. Recently, some planning-based approaches have also been proposed. These approaches work at a higher level of abstraction, but have been found to work well if complemented by low-level systems. We have identified two main difficulties for the wide use of planning techniques in this domain: generating the control models is a difficult task; and some algorithms scale poorly. In this paper we present Automated Planning for Traffic Control (APTC), a control system based on Automated Planning, that successfully overcomes these two problems. It combines techniques that continuously: learn an accurate planning model; and also divide the city for distributed reasoning in order to scale to large city networks.Experimental results show that APTC outperforms static approaches as well as other planning-based systems. We also show that the combination of both approaches improves compared with using only one of them.

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Section: Related Workmentioning

confidence: 99%

On‐line modelling and planning for urban traffic control

2021

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“…Therefore, the safety of other level crossings is higher, so a reduction in closure times hypothetically can be reduced without severe safety concerns. Reducing the time of the level crossing closure can increase safety [ 32 ] and lower social costs such as air pollution [ 33 ]. The study [ 34 ] conducted in India proved that the closure time of the level crossing has a significant impact on road-user Level-of-Service perception.…”

Section: Introductionmentioning

confidence: 99%

A New Form of Train Detection as a Solution to Improve Level Crossing Closing Time

Zawodny

Kruszyna

Szczepanek

et al. 2023

Sensors

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The critical points on the rail and road network are their intersections, i.e., level crossings. During a train crossing, car traffic is stopped. This reduces the fluidity of traffic on the road and, consequently, can cause congestion. The problem increases with the number of cars and trains. Frequently, due to national regulations, level crossing closure times are long. It is mainly dictated by safety issues. Building two-level intersections is not always a good solution, mainly because of the high cost of implementation. In the article, the authors proposed the use of sensors to reduce level crossing closure times and improve the Level of Service on the road network. The analyzed railroad lines are local agglomeration lines, mainly due to safety (low speed of commuter trains) and high impact on the road network. The sensors proposed in the article are based on radar/LIDAR. Formulas similar to HCM methods are proposed, which can be implemented in a railroad crossing controller. Simulations using the PTV Vissim program are carried out and the results are worked out based on the obtained data. The considered method can reduce the level crossing closure time by 68.6%, thereby increasing the Level of Service on roads near railroads.

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“…The rapid economic growth and the increased development of motorized transport has led to negative impacts on the environment and society [1]. Demand exceeds infrastructure capacity, generating congestion and traffic delays, high fuel consumption, a rise in greenhouse gas emissions and more accidents.…”

Section: Introductionmentioning

confidence: 99%

Connected Intelligent Transportation System Model to Minimize Societal Cost of Travel in Urban Networks

Koukounaris,

Stephanedes

2023

Sustainability

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The increasing societal cost of vehicle travel in urban networks is causing higher social and environmental impacts on road users and urban residents. The societal cost of travel can be reduced through implementation of more efficient traffic management solutions, deeper integration of connected vehicles in the traffic stream, and increased deployment of vehicle-to-infrastructure (V2I) systems. This work proposes an innovative traffic management solution, based on Urban Connected Intelligent Transport Systems. The solution dynamically manages traffic by controlling for speed and acceleration in connected vehicles through V2I to minimize societal cost in urban networks. This is achieved by minimizing all four components of societal cost of travel, i.e., traffic accidents, fuel consumption, pollutant emissions and travel time. By minimizing societal cost, this research contributes to safer, greener and more sustainable transport in urban networks, while reducing the adverse environmental and economic impact. Experimental and field data as well as data from simulation were used to test the proposed solution at an urban coastal area in Patras, Greece.

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The positive impacts of designing transition between traffic signal plans considering social cost

Cited by 10 publications

References 18 publications

On‐line modelling and planning for urban traffic control

On‐line modelling and planning for urban traffic control

A New Form of Train Detection as a Solution to Improve Level Crossing Closing Time

Connected Intelligent Transportation System Model to Minimize Societal Cost of Travel in Urban Networks

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