Paradoxes of reservation-based intersection controls in traffic networks

Levin, Michael W.; Boyles, Stephen D.; Patel, Rahul

doi:10.1016/j.tra.2016.05.013

Cited by 85 publications

(53 citation statements)

References 10 publications

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“…It can be seen from this chart that vehicle delay gain is significant, between 96-100% under low traffic. What stands out in the chart is that traffic light control starts to perform better as traffic flow increases which is also supported by [25]. Closer inspection of the data shows that CAV penetration rate also has a positive effect on the vehicle delay.…”

Section: Resultssupporting

confidence: 55%

An analysis of vehicle-to-infrastructure communications for non-signalised intersection control under mixed driving behaviour

Budan

Hayatleh

Morrey

et al. 2018

Analog Integr Circ Sig Process

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Intersection control has an important role in the management of urban traffic to ensure safety, high traffic flow and to prevent congestion. Recently, a growing body of literature has been reported on the theme of non-signalised intersection control in which traffic lights are replaced with intelligent road side units. Data from several studies suggest that non-signalised control could reduce vehicle delays and fuel consumption significantly whilst ensuring safety. However, there is little published data on the impact of the mixed driving behaviour with human-driven vehicles and Autonomous Vehicles (AV). This paper investigates the emerging role of connectivity and vehicle autonomy in the context of traffic control under the mixed driving behaviour scenario. The concepts of Vehicle-to-Infrastructure (V2I) communications and Multi-Agent Systems (MAS) are central to achieving a robust and reliable traffic-light-free intersection control. Comprehensive computer simulation results on a four-way intersection indicate over 96% reduced average vehicle delay and 37% less fuel consumption with the nonsignalised control solution compared to the traffic light control. The outcome of this study offers some important insights into enabling cooperation between vehicles and traffic infrastructure via V2I communications, in order to make more efficient real-time decisions about traffic conditions, whilst ensuring a higher degree of safety.

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Section: Resultssupporting

confidence: 55%

An analysis of vehicle-to-infrastructure communications for non-signalised intersection control under mixed driving behaviour

Budan

Hayatleh

Morrey

et al. 2018

Analog Integr Circ Sig Process

View full text Add to dashboard Cite

show abstract

“…The efficiency of reservationbased intersection controls in reducing delays was also demonstrated by Fajardo, Au, Waller, Stone, and Yang (2012), Li, Chitturi, Zheng, Bill, and Noyce (2013) and Levin, Fritz, and Boyles (2016). Yet, Levin, Boyles, and Patel (2016) indicated some cases that optimized signals can outperform reservation-based intersection controls (e.g. in local road-arterial intersections) and thus, these researchers recommended a network-based analysis before any decision about replacement of traffic signals is taken.…”

Section: Literature Resultsmentioning

confidence: 84%

Policy and society related implications of automated driving: A review of literature and directions for future research

Milakis

Arem

Wee

2017

Journal of Intelligent Transportation Systems

725

383

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KEYWORDSautomated driving; first, second, and third order impacts; policy and societal implications; ripple effect ABSTRACTIn this paper, the potential effects of automated driving that are relevant to policy and society are explored, findings discussed in literature about those effects are reviewed and areas for future research are identified. The structure of our review is based on the ripple effect concept, which represents the implications of automated vehicles at three different stages: first-order (traffic, travel cost, and travel choices), second-order (vehicle ownership and sharing, location choices and land use, and transport infrastructure), and third-order (energy consumption, air pollution, safety, social equity, economy, and public health). Our review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial. The magnitude of these benefits will likely increase with the level of automation and cooperation and with the penetration rate of these systems. The synergistic effects between vehicle automation, sharing, and electrification can multiply these benefits. However, studies confirm that automated vehicles can induce additional travel demand because of more and longer vehicle trips. Potential land use changes have not been included in these estimations about excessive travel demand. Other third-order benefits on safety, economy, public health and social equity still remain unclear. Therefore, the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.

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“…Fajardo et al [10] and Li et al [21] compared FCFS with optimized traffic signals, and found that FCFS reduced delays in certain scenarios. However, Levin et al [19] found that FCFS created more delay with signals in certain common cases (e.g. asymmetric intersections).…”

Section: Intersection Management With Autonomous Vehiclesmentioning

confidence: 99%

Blue phase: Optimal network traffic control for legacy and autonomous vehicles

Rey

Levin

2019

Transportation Research Part B: Methodological

Self Cite

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With the forecasted emergence of autonomous vehicles in urban traffic networks, new control policies are needed to leverage their potential for reducing congestion. While several efforts have studied the fully autonomous traffic control problem, there is a lack of models addressing the more imminent transitional stage wherein legacy and autonomous vehicles share the urban infrastructure. We address this gap by introducing a new policy for stochastic network traffic control involving both classes of vehicles. We conjecture that network links will have dedicated lanes for autonomous vehicles which provide access to traffic intersections and combine traditional green signal phases with autonomous vehicle-restricted signal phases named blue phases. We propose a new pressure-based, decentralized, hybrid network control policy that activates selected movements at intersections based on the solution of mixed-integer linear programs. We prove that the proposed policy is stable, i.e. maximizes network throughput, under conventional travel demand conditions. We conduct numerical experiments to test the proposed policy under varying proportions of autonomous vehicles. Our experiments reveal that considerable trade-offs exist in terms of vehicle-class travel time based on the level of market penetration of autonomous vehicles. Further, we find that the proposed hybrid network control policy improves on traditional green phase traffic signal control for high levels of congestion, thus helping in quantifying the potential benefits of autonomous vehicles in urban networks.

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Paradoxes of reservation-based intersection controls in traffic networks

Cited by 85 publications

References 10 publications

An analysis of vehicle-to-infrastructure communications for non-signalised intersection control under mixed driving behaviour

An analysis of vehicle-to-infrastructure communications for non-signalised intersection control under mixed driving behaviour

Policy and society related implications of automated driving: A review of literature and directions for future research

Blue phase: Optimal network traffic control for legacy and autonomous vehicles

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