Constrained dynamic control of traffic junctions

Excessive traffic in our urban environments has detrimental effects on our health, economy and standard of living. To mitigate this problem, an adaptive traffic lights signalling scheme is developed and tested in this paper. This scheme is based on a state space representation of traffic dynamics, controlled via a dynamic programme. To minimise implementation costs, only one loop detector is assumed at each link. The comparative advantages of the proposed system over optimal fixed time control are highlighted through an example. Results will demonstrate the flexibility of the system when applied to different junctions. Monte Carlo runs of the developed scheme highlight the consistency and repeatability of these results.peer-reviewe

Adaptive traffic signal control using approximate dynamic programming

This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach

Exploratory Analysis of Connected Fully Autonomous Vehicles on the Safety and Efficiency of Road Networks using Microsimulation

The research had set out to explore the effects of the widespread introduction of driverless technology by using publicly available data and assessing the changes it brings to the efficiency and safety of the road network. ConFAVs were slowly introduced to the network and average vehicle delays and the level of service (LOS) of links observed, followed by a surrogate safety assessment. Two published behaviour models (Atkins and CoEXist), and a third model (Tested Logic) was created, which accounted for a change in ConFAV behaviour while following another ConFAV. A comparison of the change in the average vehicle delay and the total number of serious conflicts recorded, highlighted that the CoEXist behavioural model had performed the best in three types of junctions and was used to further analyse the case study. The case study involved 2 small, isolated networks within the Queen Elizabeth Olympic Park Area of London (‘Site A’ was residential and ‘Site B’ was commercial). ‘Site A’ performed well with delays but performed poorly when comparing the number of recorded conflicts against the increasing numbers of ConFAVs. ‘Site B’ showed limited improvement in LOS and performed poorly in the safety analysis as the number of recorded conflicts increased fourfold in some scenarios. The results of the case study led to a conclusion that increased numbers of ConFAVs driving in platoons within the network could reduce delays and as a result either maintained the LOS of the chosen route or made it better. The lead vehicle in the platoon was able to anticipate changes in signals and communicate this with the trailing vehicles, allowing them to perform better at signalised junctions. Platoons also increased network capacity on congested links allowing better performance in the average delays, as observed in Case Study B. However, greater numbers of platoons resulted in larger numbers of rear-end conflicts when a surrogate safety analysis was performed using Time to Collision (TTC) as a parameter. Thus, it was recommended that another method is used to investigate potential conflicts that could recognise and account for platoons

Accident Analysis and Prevention: Course Notes 1987/88

This report consists of the notes from a series of lectures given by the authors for a course entitled Accident Analysis and Prevention. The course took place during the second term of a one year Masters degree course in Transport Planning and Engineering run by the Institute for Transport Studies and the Department of Civil Engineering at the University of Leeds. The course consisted of 18 lectures of which 16 are reported on in this document (the remaining two, on Human Factors, are not reported on in this document as no notes were provided). Each lecture represents one chapter of this document, except in two instances where two lectures are covered in one chapter (Chapters 10 and 14). The course first took place in 1988, and at the date of publication has been run for a second time. This report contains the notes for the initial version of the course. A number of changes were made in the content and emphasis of the course during its second run, mainly due to a change of personnel, with different ideas and experiences in the field of accident analysis and prevention. It is likely that each time the course is run, there will be significant changes, but that the notes provided in this document can be considered to contain a number of the core elements of any future version of the course

Adaptive traffic signal control using approximate dynamic programming

This thesis presents a study on an adaptive traffic signal controller for real-time operation. An approximate dynamic programming (ADP) algorithm is developed for controlling traffic signals at isolated intersection and in distributed traffic networks. This approach is derived from the premise that classic dynamic programming is computationally difficult to solve, and approximation is the second-best option for establishing sequential decision-making for complex process. The proposed ADP algorithm substantially reduces computational burden by using a linear approximation function to replace the exact value function of dynamic programming solution. Machine-learning techniques are used to improve the approximation progressively. Not knowing the ideal response for the approximation to learn from, we use the paradigm of unsupervised learning, and reinforcement learning in particular. Temporal-difference learning and perturbation learning are investigated as appropriate candidates in the family of unsupervised learning. We find in computer simulation that the proposed method achieves substantial reduction in vehicle delays in comparison with optimised fixed-time plans, and is competitive against other adaptive methods in computational efficiency and effectiveness in managing varying traffic. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised. The proposed ADP algorithm is in compliance with a range of discrete systems of resolution from 0.5 to 5 seconds per temporal step. This study demonstrates the readiness of the proposed approach for real-time operations at isolated intersections and the potentials for distributed network control

Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc