Abstract

Everyday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. Building route choice sets is a difficult task. Even including detailed attributes such as the number of left turns, the number of speed bumps, distance and other route choice properties we still see that choice set quality measures suggest poor replication of observed paths. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which could potentially be considered to be important for route choice in a similar way. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions are shown to be significantly different so that the choice sets do not reflect the traveler preferences, this is in line with classical choice set quality indicators. Secondly, we investigate often used choice set quality methods and formulate measures that are less sensitive to small differences between routes that can be argued to be insignificant or irrelevant. Such difference may be partially due to inaccuracy in map-matching observations to dense urban road networks.

Highlights

  • Route choice models play an important role in many transport applications and help to understand why people travel the way they do and to predict what they will do in the future

  • Most models are based on multinomial logistic regression (MNL) and correction factors are introduced to account for correlation between overlapping routes

  • Recursive logit (RL) models described by Fosgerau[4] and by Mai[9] do not require a choice set for model estimation

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Summary

Introduction

Route choice models play an important role in many transport applications and help to understand why people travel the way they do and to predict what they will do in the future. The complexity of the routes in the generated choice sets does not reflect the traveler behavior we observed in the paths chosen by cyclists.

Background
Route complexity
Route complexity in real-life GPS traces
Collecting data of bicycle movements
Generating route choice sets
Run-times
Route complexity in generated routes
Choice set quality assessment
Coverage based on link matching
Coverage based on geometry buffering
Distance measures and quality assessment
Conclusions and recommendations
Findings
Compliance with ethical standards
ETH-Zurich
Full Text
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