Abstract

Efficient public transport (PT) networks are vital for well-functioning and sustainable cities. Compared to other modes of transport, PT networks feature inherent systemic complexity due to their schedule-dependence and network organization. Because of this, efficient PT network planning and management calls for advanced modeling and analysis tools. These tools have to take into account how people use PT networks, including factors such as demand, accessibility, trip planning and navigability. From the PT user perspective, the common criteria for planning trips include waiting times to departure, journey durations, and the number of required transfers. However, waiting times and transfers have typically been neglected in PT accessibility studies and related decision-support tools. Here, we tackle this issue by introducing a decision-support framework for PT planners and managers, based on temporal networks methodology. This framework allows for computing pre-journey waiting times, journey durations, and number of required transfers for all Pareto-optimal journeys between any origin–destination pair, at all points in time. We visualize this information as a temporal distance profile, covering any given time interval. Based on such profiles, we define the best-case, mean, and worst-case measures for PT travel time and number of required PT vehicle boardings, and demonstrate their practical utility to PT planning through a series of accessibility case studies. By visualizing the computed measures on a map and studying their relationships by performing an all-to-all analysis between 7463 PT stops in the Helsinki metropolitan region, we show that each of the measures provides a different perspective on accessibility. To pave the way towards more comprehensive understanding of PT accessibility, we provide our methods and full analysis pipeline as free and open source software.

Highlights

  • Efficient, easy-to-use public transport (PT) networks are a vital element of functional, sustainable cities (Banister, 2008; Newman & Kenworthy, 1989)

  • We describe how we compute the sets of Pareto-optimal journey alternatives based on General Transit Feed Specification (GTFS) and OpenStreetMap data, and describe our analysis pipeline in more detail

  • For the purposes of PT planning, this paper introduced an approach for computing PT travel times and required numbers of transfers based on the analysis of Pareto-optimal journey alternatives

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Summary

Introduction

Easy-to-use public transport (PT) networks are a vital element of functional, sustainable cities (Banister, 2008; Newman & Kenworthy, 1989). Even though sampling yields an approximate picture of the dynamic travel time profile, disentangling pre-journey waiting times from journey durations remains difficult These challenges can be overcome by realizing that PT travel times and numbers of required boardings are determined by the journey alternatives enabled by the PT network, assessed through the concept of Pareto-optimality. The Pareto-frontier contains the fastest journey alternatives for reaching the destination with different numbers of boardings, at all departure times Such sets of Pareto-optimal journey alternatives fully describe the dynamic accessibility between origin–destination pairs in terms of journey durations, pre-journey waiting times, and transfers. We show how sets of Pareto-optimal journey alternatives can be used to construct temporal distance profiles that provide full temporal information on the time to reach a destination over a specified time interval (Pan & Saramäki, 2011) These profiles can be augmented with information on the required numbers of vehicle boardings. Metropolitan area, revealing general relationships between the different definitions on PT travel time and the number of required PT vehicle boardings

Methods
Fastest-path temporal distance profiles
Fastest-path temporal distance statistics
Boarding-count-augmented temporal distance profiles
Boarding-count statistics and time-transfer trade-offs
Computation of Pareto-optimal journeys
Analysis pipeline
Software
Setup for this study
Results
Access to Aalto University campus
Service level variations through a day
Access to multiple long-distance train stations
All-to-all rush hour analysis
Notes on running times
Discussion
Full Text
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