Path optimization of taxi carpooling

Abstract

The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers.

The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

“…As indicated by another study, time pressures, when combined with traffic congestion, can cause driver's aggressive driving behaviors [46,47]. Both time pressures and traffic congestion are common during the peak-hour period which is consistent with previous studies [48].…”

“…This can be explained by the fact that drivers may drive more recklessly with aggressive driving behaviors in open space areas compared to high population density areas [49]. In addition, an explanation for this interesting result is that operating speeds in an open space area may tend to be higher than in other areas as open space areas generally have lower traffic densities which increases the likelihood of high level injury severity given an accident happened [48]. For control device types, consistent with previous studies [18,50], passive control was found positively correlated with high level injury severity for drivers with aggressive driving behaviors which is demonstrated by our research.…”

A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China

“…On the contrary, public transit, with uncertain waiting time and fixed routes, has a limitation to undertake multiple activities in a tour [21,22]. Therefore, the complex trip chains may increase the dependence of travelers on automobiles, which leads to the problems related to auto route choice and optimization, as well as transportation safety [23][24][25][26][27][28][29][30][31]. In order to verify this conclusion, this paper also takes the mode choice of commuters as one of the independent variables.…”

A Bi-Level Programming Model of Liquefied Petroleum Gas Transportation Operation for Urban Road Network by Period-Security

“…The bi-level programming model is NP-hard (Bard J et al [58]) and several generally used solution methods, such as KKT-based (Kara B Y et al [59], Bianco et al [60]) and duality-based optimization approaches (Marcotte et al [61]), could possibly lead to locally optimal solution (Vicente L et al [62]). The Genetic Algorithm (GA) is characterized by global superiority and dramatic convergence, and its essence is the simulation of nature's survival of the fittest (Changxi Ma et al [63]). Additionally, the lower problem is a UE model, and the Frank-Wolfe algorithm is widely regarded as an efficient method.…”