Incorporating observed and unobserved heterogeneity in route choice analysis with sampled choice sets

2016 ◽  
Vol 67 ◽  
pp. 31-46 ◽  
Author(s):  
Dawei Li ◽  
Tomio Miwa ◽  
Takayuki Morikawa ◽  
Pan Liu
Keyword(s):  
Unobserved Heterogeneity ◽  
Route Choice ◽  
Choice Analysis ◽  
Choice Sets

Path Size Modeling in Multimodal Route Choice Analysis

2005 ◽  
Vol 1921 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Sascha Hoogendoorn-Lanser ◽  
Rob van Nes ◽  
Piet Bovy
Keyword(s):  
Route Choice ◽  
Transport Networks ◽  
Main Mode ◽  
Choice Analysis ◽  
Multimodal Transport ◽  
Choice Sets ◽  
Error Terms ◽  
Multimodal Networks ◽  
Definition Of ◽  
Size Variable

Travelers in multimodal networks make many choices (e.g., main mode, access modes, egress modes, boarding nodes, transfer nodes, and egress nodes). One way to address this complexity of choices is to analyze choice sets of multimodal routes. However, choice sets for multimodal networks are large, and overlap of routes within choice sets is substantial. This paper focuses on overlap in multimodal transport networks. An overview of the topic of overlap and route choice modeling is given and is followed by an analysis of how overlap might be defined in the context of multimodal networks. Three definitions of “overlap” are proposed, based on number of legs, time, or distance. The different definitions are analyzed using path size logit estimations, which show that path size must be accounted for. Furthermore, the definition of “path size” for multimodal transport networks should be different from that used for road networks: for multimodal transport networks, a definition using number of legs yields substantially better results. Estimation results suggest that the weighting parameter corresponding with the path size variable should be equal to 1, implying that the path size variable based on number of legs accounts for the correlation of error terms of overlapping parts.

scholarly journals Heterogeneous Choice Sets and Preferences

Econometrica ◽  
2021 ◽  
Vol 89 (5) ◽  
pp. 2015-2048
Author(s):  
Levon Barseghyan ◽  
Maura Coughlin ◽  
Francesca Molinari ◽  
Joshua C. Teitelbaum
Keyword(s):  
Unobserved Heterogeneity ◽  
Expected Utility Theory ◽  
Minimum Size ◽  
Discrete Choice Analysis ◽  
Conditional Moment ◽  
Higher Dimensional ◽  
Choice Analysis ◽  
Choice Sets ◽  
Finite Set ◽  
Feasible Sets

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity.

scholarly journals Path complexity and bicyclist route choice set quality assessment

2019 ◽  
Author(s):  
Thomas Koch ◽  
Luk Knapen ◽  
Elenna Dugundji
Keyword(s):  
Shortest Paths ◽  
Route Choice ◽  
Large Set ◽  
Map Matching ◽  
Demand Modeling ◽  
Urban Road ◽  
Modeling Process ◽  
Choice Sets ◽  
Minimum Number ◽  
Choice Set

AbstractEveryday 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.

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