Development of Traffic-Based Congestion Pricing and Its Application to Automated Vehicles

Improved traffic management techniques are needed to reduce congestion on road networks, especially as “driving” is made easier, through self-driving vehicles. In this paper, reactive congestion pricing varies toll rates based on recent congestion levels, and automated vehicles are added to the conventional traffic mix for evaluation of evolving travel conditions. As expected, drivers with higher values of travel time (VOTT) are more likely to use the tolled route than drivers with lower VOTT, and tolled-route speeds rose (about 4%) while speeds on non-tolled road segments fell (about 15%). Thanks to traveler sorting, net benefits exceeded $600 per hour in all scenarios, using a very small (toy) network. Toll revenues can be distributed uniformly among travelers (resulting in credit-based congestion pricing) or invested in improving bottlenecks and alternative modes. Rising shares of automated vehicles (from 0% to 50% and 100%) also improved outcomes.

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Capacity in municipalities: Infrastructures, maintenance debts and ways of overcoming a run-to-failure mentality

Local Economy The Journal of the Local Economy Policy Unit ◽

10.1177/02690942211030475 ◽

2021 ◽

pp. 026909422110304

Author(s):

Jens Alm ◽

Alexander Paulsson ◽

Robert Jonsson

Keyword(s):

Economies Of Scale ◽

Heavy Traffic ◽

Road Networks ◽

Large Degree ◽

Welfare States ◽

The Road ◽

Technical Capacity ◽

Multidimensional Concept ◽

Sewage Systems ◽

Road Segments

There is a growing maintenance debt of ageing and critical infrastructures in many municipalities in European welfare states. In this article, we use the multidimensional concept of local capacity as a point of departure to analyse how and in what ways Swedish municipalities work with the routine maintenance of infrastructures, including municipal road networks as well as water and sewage systems. For the road networks, maintenance is generally outsourced to contractors and there is also a large degree of tolerance for various standards on different road segments within and between the municipalities. Less used road segments are not as prioritised as those with heavy traffic. For the water and sewage systems, in-house technical capacity is needed as differences in water quality are not tolerated. Economies of scale mean that in-house capacity is translated into the creation of inter-municipal bodies. As different forms of capacities tend to reinforce each other, municipal capacity builds up over time in circular movements. These results add knowledge to current research by pointing to the ways municipalities are overcoming a run-to-failure mentality by building capacity to pay off the infrastructural maintenance debt.

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Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2014/829256 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Zhiming Gui ◽

Haipeng Yu

Keyword(s):

Travel Time ◽

Extreme Learning Machine ◽

Road Networks ◽

Time Estimation ◽

Learning Ability ◽

Prediction Algorithm ◽

Travel Time Estimation ◽

Trace Data ◽

Learning Machine ◽

Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

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Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994580 ◽

2021 ◽

pp. 036119812199458

Author(s):

Slobodan Gutesa ◽

Joyoung Lee ◽

Dejan Besenski

Keyword(s):

Travel Time ◽

Information Exchange ◽

Fixed Time ◽

Optimization Method ◽

Time Signal ◽

Market Penetration ◽

Automated Vehicles ◽

Automated Driving ◽

Concept Evaluation ◽

Intersection Management

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

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Road Traffic Congestion Management Based on a Search-Allocation Approach

Transport and Telecommunication Journal ◽

10.1515/ttj-2017-0003 ◽

2017 ◽

Vol 18 (1) ◽

pp. 25-33 ◽

Cited By ~ 4

Author(s):

Jamal Raiyn

Keyword(s):

Travel Time ◽

Real Time ◽

Traffic Congestion ◽

Traffic Management ◽

Road Traffic ◽

Smart Cities ◽

Self Regulation ◽

Congestion Management ◽

Shortest Route ◽

Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

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