Improving the Performance of Transportation Networks: A Semi-Centralized Pricing Approach

Cao, Zhiguang; Guo, Hongliang; Song, Wen; Gao, Kaizhou; Kang, Liujiang; Zhang, Xuexi; Wu, Qilun

doi:10.1109/tits.2020.2991759

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Perhaps one of the closest strategies to our solution is pricing mechanisms in the literature. Road pricing policies, such as assigning a fee or tax for driving on a highway/road, have been widely studied in theory and practice [7][8][9][10][11][12][13][14][15][16]. In this approach, congestion reduction is expected as a result of discouraging people to use congested roads.…”

Section: Introductionmentioning

confidence: 99%

Congestion Reduction via Personalized Incentives

Ghafelebashi¹,

Razaviyayn²,

Dessouky³

2022

Preprint

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With rapid population growth and urban development, traffic congestion has become an inescapable issue, especially in large cities. Many congestion reduction strategies have been proposed in the past, ranging from roadway extension to transportation demand management. In particular, congestion pricing schemes have been used as negative reinforcements for traffic control. In this project, we study an alternative approach of offering positive incentives to drivers to take different routes. More specifically, we propose an algorithm to reduce traffic congestion and improve routing efficiency via offering personalized incentives to drivers. We exploit the wide-accessibility of smart devices to communicate with drivers and develop an incentive offering mechanism using individuals' preferences and aggregate traffic information. The incentives are offered after solving a large-scale optimization problem in order to minimize the total travel time (or minimize any cost function of the network such as total Carbon emission). Since this massive size optimization problem needs to be solved continually in the network, we developed a distributed computational approach. The proposed distributed algorithm is guaranteed to converge under a mild set of assumptions that are verified with real data. We evaluated the performance of our algorithm using traffic data from the Los Angeles area. Our experiments show congestion reduction of up to 11% in arterial roads and highways.

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Section: Introductionmentioning

confidence: 99%

Congestion Reduction via Personalized Incentives

Ghafelebashi¹,

Razaviyayn²,

Dessouky³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…With the IoT and highly automated systems, transportation management is shifting from active control to passive observation. In addition, traffic management to reduce congestion is a crucial topic in the smart city context, and numerous studies related to this topic were published [29,30]. A smart transportation system, or intelligent transportation system (ITS), focuses on economy and society interest, reduced travel times [31], arrival on time [32], fuel consumption, and pollution, as well as improving traffic safety.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Total Transportation Management System for Future Smart Cities

Nguyen

Rohács

et al. 2020

Applied Sciences

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Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

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A diversity-enhanced memetic algorithm for solving electric vehicle routing problems with time windows and mixed backhauls

Xiao

Cao

et al. 2023

Applied Soft Computing

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Improving the Performance of Transportation Networks: A Semi-Centralized Pricing Approach

Cited by 5 publications

References 37 publications

Congestion Reduction via Personalized Incentives

Congestion Reduction via Personalized Incentives

Intelligent Total Transportation Management System for Future Smart Cities

A diversity-enhanced memetic algorithm for solving electric vehicle routing problems with time windows and mixed backhauls

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