A Multi-Modal Route Choice Model with Ridesharing and Public Transit

Li, Meng; Hua, Guowei; Huang, Hai-Jun

doi:10.3390/su10114275

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…Li et al investigate the impacts of OD-based surging pricing on the ridesharing market [22]. Li et al propose a convex programming approach to describe the ridesharing equilibrium network and provide the algorithm [23].…”

Section: Introductionmentioning

confidence: 99%

A Scheme to Improve Network Performance Based on Traffic Restriction and Pricing in the Presence of Carpooling

Chong¹,

Zhu

et al. 2022

Mathematical Problems in Engineering

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For the purpose of optimizing the traffic network in the presence of carpooling, a traffic demand management scheme was proposed with the consideration of traffic restriction, carpooling, and road pricing. We establish a variational inequality describing the travelers’ mode and route choice following the logit mode split and multimodal user equilibrium principle in the condition of elastic demand. A bilevel programming was designed with the variational inequality as the lower level and optimization objective function taking into account traffic congestion and social welfare as the upper level. The results show that this scheme is possible to be effective in improving network performance. The higher average occupancy of carpooling vehicles and the carpooling demand may not bring better network performance due to the topology, parameters of the network, and travel demand. The fare of carpooling travelers is possible to impact the network very slightly with this scheme. Besides, the group of travelers who is able to afford more than one vehicle is hardly affected by this scheme.

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Section: Introductionmentioning

confidence: 99%

A Scheme to Improve Network Performance Based on Traffic Restriction and Pricing in the Presence of Carpooling

Chong¹,

Zhu

et al. 2022

Mathematical Problems in Engineering

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“…The multi-destination path planning algorithm combined global static and local dynamics. It obtained an optimal path planning that enabled mobile robots to stabilize, avoided obstacles in time, and accurately moved to the destination [16]. The multi-objective intelligent water drop algorithm (MO-IWD) was based on the coefficient of variation (CV).…”

Section: Introductionmentioning

confidence: 99%

Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles

Zhuang

Dong

et al. 2021

Applied Sciences

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In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.

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