Data-Driven Method to Estimate the Maximum Likelihood Space–Time Trajectory in an Urban Rail Transit System

Abstract

The Urban Rail Transit (URT) passenger travel space-time trajectory reflects a passenger's path-choice and the components of URT network passenger flow. This paper proposes a model to estimate a passenger's maximum-likelihood space-time trajectory using Automatic Fare Collection (AFC) transaction data, which contain the passenger's entry and exit information. First, a method is presented to construct a space-time trajectory within a tap in/out constraint. Then, a maximum likelihood space-time trajectory estimation model is developed to achieve two goals: (1) to minimize the variance in a passenger's walk time, including the access walk time, egress walk time and transfer walk time when a transfer is included; and (2) to minimize the variance between a passenger's actual walk time and the expected value obtained by manual survey observation. Considering the computational efficiency and the characteristics of the model, we decompose the passenger's travel links and convert the maximum likelihood space-time trajectory estimation problem into a single-quadratic programming problem. Real-world AFC transaction data and train timetable data from the Beijing URT network are used to test the proposed model and algorithm. The estimation results are consistent with the clearing results obtained from the authorities, and this finding verifies the feasibility of our approach.

Scheduling overnight trains for improving both last and first train services in an urban subway network

“…A space-time network representation has been widely used in transportation route optimization, various scheduling applications, and general dynamic network flow modeling. 21,31 Considering a physical urban rail transit network with a set of nodes (stations) N and a set of links L (segments between adjacent stations) shown in Figure 4, each link can be denoted as a directed link (i, j) from upstream station i to downstream station j, Table 2. Indices, sets, parameters, and variables.…”

Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network

“…(2004) [25] assumed that all passengers have full predictive information about present and future network conditions. Chen (2018) [7] assumed that all passengers can always board the first train arriving. Some additional input parameters are needed and have a significant impact on the accuracy of the estimation result.…”

“…As shown in Figure 3, we have extended the Beijing rail transit network topology by replacing a station with four types of spatial nodes (entry, exit, platform, and track) from which we generate the set of feasible space-timesequence trajectories using the methodology in [7]. The set of passengers' feasible space-time-sequence trajectories is the basis for solving the station time parameters estimation problem and the passenger's space-time-sequence trajectoryestimation problem.…”

“…Thus, this paper attempts to mine passenger travel patterns based on automatic fare collection (AFC) transaction data and Automatic Vehicle Location (AVL) data. It builds on our prior work on the problem [7] and reconstructs a passenger's trajectory by introducing the concept of a sequence to describe left-behind. The prior work ignored train vehicle capacity constraints and assumed that all passengers left a platform for another subway station by the first departing train after their arrival at the platform.…”

An Innovative Approach to Determining High-Risk Nodes in a Complex Urban Rail Transit Station: A Perspective of Promoting Urban Sustainability

“…(1) Formulating judgment table for the relationship of each pair of nodes [61]. If the two nodes are connected through passenger flow, then the relationship of the two nodes is marked as 1.…”