scholarly journals Dynamic Ridesharing in Peak Travel Periods

2020 ◽  
pp. 1-1
Author(s):  
Hui Luo ◽  
Zhifeng Bao ◽  
Farhana Choudhury ◽  
Shane Culpepper
Keyword(s):  
Peak Travel

Tourist management based on behaviour characteristics during peak travel period - a case study of Jiuzhaigou

2013 ◽  
Vol 51 (3/4) ◽  
pp. 222 ◽  
Author(s):  
Weimin Zheng ◽  
Peiyu Ren ◽  
Peng Ge ◽  
Qinghu An ◽  
Maozhu Jin
Keyword(s):  
Peak Travel

scholarly journals Can Road Toll Convince Car Travelers to Adjust Their Departure Times? Accounting for the Effect of Choice Behavior under Long and Short Holidays

2020 ◽  
Vol 12 (24) ◽  
pp. 10470
Author(s):  
Haiyan Zhu ◽  
Hongzhi Guan ◽  
Yan Han ◽  
Wanying Li
Keyword(s):  
Traffic Congestion ◽  
Choice Behavior ◽  
Road Traffic ◽  
Stated Preference ◽  
Revealed Preference ◽  
The Road ◽  
Departure Time ◽  
Departure Time Choice ◽  
Mnl Model ◽  
Peak Travel

The adjustment of road toll is an important measure that can alleviate road traffic congestion by convincing car travelers to travel during off-peak times. In order to reduce congestion on the expressway on the first day of a holiday, factors that affect the departure times of holiday travelers must be comprehensively understood to determine the best strategy to persuade car travelers to avoid peak travel times. This paper takes holiday car travelers as the research object and explores the characteristics and rules of departure time choice behavior for different holiday lengths. Based on Utility Maximization Theory, a multinomial logit (MNL) model of departure time choice for a three-day short holiday and a seven-day long holiday was established. Model calibration and elastic analysis were carried out using Revealed Preference/Stated Preference (RP/SP) survey data. Additionally, the influence of the highway toll policy on departure times for long and short holidays was analyzed. The results show that the rate of first-day departures is much higher than that of other departure times for both short and long vacations under the current policy of free holiday passage on highways. Factors such as trip duration, size of the tourist group, the number of visits, travel range, travel time, monthly income, occupation, age and road toll have a significant influence on the departure time decisions of holiday car travelers, and the effect and degree of influence are markedly different for different holiday lengths. The effects of tolls for each departure time and different pricing scenarios on the choice behavior of travelers are different between long and short holidays. Furthermore, the effectiveness of the road toll policy also varies for travelers with different travel distances. This study can provide useful information for the guidance of holiday travelers, the management of holiday tolls on expressways and the formulation of holiday leave time.

Transit Signal Priority Along Arterials Using Advanced Detection

2003 ◽  
Vol 1856 (1) ◽  
pp. 220-230 ◽  
Author(s):  
Yann Wadjas ◽  
Peter G. Furth
Keyword(s):  
Control Strategies ◽  
Traffic Density ◽  
Light Rail ◽  
Transit Signal Priority ◽  
Adaptation Algorithm ◽  
Peer Communication ◽  
Length Estimation ◽  
Rail Line ◽  
Peak Travel ◽  
Green Period

This research developed and tested the concept of advanced detection and cycle length adaptation as a strategy for providing priority for transit vehicles. In a departure from control strategies that rely on detection only a few seconds in advance of the stopline, a control algorithm was developed in which transit vehicles are detected two to three cycles in advance of their arrival at an intersection stopline, and phase lengths were then constrained so that the transit-serving phase was green for a 40-s predicted arrival window. Methods were developed for selecting whether to extend or compress phase lengths to shift a green period to cover the arrival window. Adaptive control was combined with actuated control using traffic density and queue length estimation, transit stopline actuation, and peer-to-peer communication for coordination in the peak travel direction. The method was applied by simulation to Boston, Massachusetts’ Huntington Avenue corridor, which is served by a light-rail line running partly in mixed traffic and partly in a median reservation. The prediction/adaptation algorithm resulted in 82% of the trains arriving during the green phase. This control strategy resulted in substantial improvements to transit travel time and regularity with negligible impacts on private traffic and pedestrians, and was found to be more effective than simple preemption.

The Robust Traveling Salesman Problem with Time Windows Under Knapsack-Constrained Travel Time Uncertainty

2020 ◽  
Author(s):  
Enrico Bartolini ◽  
Dominik Goeke ◽  
Michael Schneider ◽  
Mengdie Ye
Keyword(s):  
Travel Time ◽  
Traveling Salesman Problem ◽  
Upper Bound ◽  
Time Windows ◽  
Traveling Salesman ◽  
Time Uncertainty ◽  
Uncertainty Set ◽  
Travel Time Uncertainty ◽  
The Impact ◽  
Peak Travel

We study the traveling salesman problem with time windows (TSPTW) under travel time uncertainty—modeled by means of an uncertainty set including all travel time vectors of interest. We consider a knapsack-constrained uncertainty set stipulating a nominal and a peak travel time for each arc and an upper bound [Formula: see text] on the sum of all deviations from the nominal times. Viewing the difference between the peak time and its nominal value as the maximum delay possibly incurred when traversing the corresponding arc, the problem we consider is thus to find a tour that remains feasible for up to [Formula: see text] units of delay. This differs from previous studies on robust routing under travel time uncertainty, which have relied on cardinality-constrained sets and only allow for an upper bound on the number of arcs with peak travel time. We propose an exact algorithm based on column generation and dynamic programming that involves effective dominance rules and an extension of the [Formula: see text]-tour relaxation proposed in the literature for the classical TSPTW. The algorithm is able to solve the robust TSPTW under both knapsack- and cardinality-constrained travel time uncertainty. Extensive computational experiments show that the algorithm is successful on instances with up to 80 customers. In addition, we study the impact of the two uncertainty sets on the trade-off between service quality and cost exhibited by the resulting solutions.

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