Potential travel cost saving in urban public-transport networks using smartphone guidance.

Cuiying Song,Jihui Ma,Wei Guan,Xiaolei Ma

doi:10.1371/journal.pone.0197181

Abstract

Public transport (PT) is a key element in most major cities around the world. With the development of smartphones, available journey planning information is becoming an integral part of the PT system. Each traveler has specific preferences when undertaking a trip, and these preferences can also be reflected on the smartphone. This paper considers transit assignment in urban public-transport networks in which the passengers receive smartphone-based information containing elements that might influence the travel decisions in relation to line loads, as well as passenger benefits, and the paper discusses the transition from the current widespread choosing approach to a personalized decision-making approach based on smartphone information. The approach associated with smartphone guidance that considers passengers’ preference on travel time, waiting time and transfer is proposed in the process of obtaining his/her preferred route from the potential travel routes generated by the Deep First Search (DFS) method. Two other approaches, based on the scenarios reflecting reality, include passengers with access to no real time information, and passengers that only have access to the arrival time at the platform are used as comparisons. For illustration, the same network proposed by Spiess and Florian is utilized on the experiments in an agent-based model. Two experiments are conducted respectively according to whether each passenger’s choosing method is consistent. As expected, the results in the first experiment showed that the travel for consistent passengers with smartphone guidance was clearly shorter and that it can reduce travel time exceeding 15% and weighted cost exceeding 20%, and the average saved time approximated 3.88 minutes per passenger. The second experiment presented that travel cost, as well as cost savings, gradually decreased by employing smartphone guidance, and the maximum cost savings accounted for 14.2% of the total weighted cost.

Highlights

Public transit systems play an important role in transporting passengers in metropolitan areas [1] and are coping with an increasing demand for the existing transit network, which is currently one of the largest transport challenges
The focus in this paper is to provide a detailed discussion on the impact of the three decision-making approaches on passengers’ decisions, such as departure time, stop choice and route choice built on an illustrative example referring to the same network that Spiess and Florian [10] used to describe the optimal behavior of passengers without access to information
Transit assignment in public transport using smartphone guidance were having access to no travel information, the vehicle arrival information at the platform, and travel route strategies provided by a smartphone to minimize the travel time from origin to destination

Summary

Introduction

Public transit systems play an important role in transporting passengers in metropolitan areas [1] and are coping with an increasing demand for the existing transit network, which is currently one of the largest transport challenges. Intelligent Transportation Systems (ITS) and Advanced Public Transport Systems (APTS) are able to provide timely information to transit users on the conditions of the network, such as lines, schedule, arrival time, departure time, occupancy and transfer. This information can be available before trip departure (pre-trip) or during the trip (en-route) and be delivered via a wide variety of media, such as audible or visual messages through at-stop or in-vehicle information devices, the Internet, and through smartphones, which are a widespread communication tool, to individual transit users or users’ groups. More predictive real-time information, especially crowding information on board, can attract more PT users to choose a more comfortable and efficient travel route [7] based on the transit environment [8]

Literature review

Experiments and results

Findings

Conclusions