Impact of Route Choice Set on Route Choice Probabilities

2008 ◽  
Vol 2076 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Michiel C. J. Bliemer ◽  
Piet H. L. Bovy
Keyword(s):  
Route Choice ◽  
Choice Probabilities ◽  
Choice Set

scholarly journals A link based network route choice model with unrestricted choice set

2013 ◽  
Vol 56 ◽  
pp. 70-80 ◽  
Author(s):  
Mogens Fosgerau ◽  
Emma Frejinger ◽  
Anders Karlstrom
Keyword(s):  
Choice Model ◽  
Route Choice ◽  
Choice Set

scholarly journals Road-safety recognition and network equilibrium with perceived route-choice sets

2019 ◽  
Vol 1 (2) ◽  
pp. 126-134
Author(s):  
Mao-sheng Li ◽  
He-lai Huang
Keyword(s):  
Convex Combination ◽  
Route Choice ◽  
User Equilibrium ◽  
Equilibrium Analysis ◽  
Combination Method ◽  
Crash Frequency ◽  
Traffic Demand ◽  
Additional Costs ◽  
Choice Set

Abstract Safety is regarded as the second basic need in Maslow’s hierarchy of needs (1943), and safety recognition and circumvention behaviour in the route-choice decision-making process should therefore be accommodated in network-traffic equilibrium analysis frameworks. This paper proposes a framework by which crash frequency, forecasted using the safety-analysis method or compiled from historical data for intersections, is used to measure the safety consciousness of drivers. Drivers are then classified into different groups according to their acceptable-risk thresholds, and each group has its own route-choice set. Decision behaviour whereby drivers are willing to bear additional costs in order to circumvent travel risk is incorporated into the variational inequality model based on the user equilibrium in the perceived route-choice set (UE-PRCS), which is an extension of Wardrop’s first principle. The Frank–Wolfe algorithm, based on the convex combination method, is employed to obtain the solution. A small road network is used as a case study to illustrate the proposed framework, incorporating risk recognition and circumvention behaviour under different combinations of traffic demand and risk-sensitivity group ratio. The results show that the standard user equilibrium is a special case of the UE-PRCS, but that the UE traffic state is more common than the UE-PRCS under different parameters.

STOCHASTIC ROUTE CHOICE SET GENERATION: BEHAVIORAL AND PROBABILISTIC FOUNDATIONS

2007 ◽  
Vol 3 (3) ◽  
pp. 173-189 ◽  
Author(s):  
Piet H.L. Bovy ◽  
Stella Fiorenzo-Catalano
Keyword(s):  
Route Choice ◽  
Choice Set Generation ◽  
Choice Set

Geographically Stratified Importance Sampling for the Calibration of Aggregated Destination Choice Models for Trip Distribution

2005 ◽  
Vol 1935 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Min-Tang Li ◽  
Lee-Fang Chow ◽  
Fang Zhao ◽  
Shi-Chiang Li
Keyword(s):  
Importance Sampling ◽  
Random Sampling ◽  
Sampling Strategy ◽  
Simple Random Sampling ◽  
Choice Models ◽  
Stratified Sampling ◽  
Destination Choice ◽  
Selection Probabilities ◽  
Choice Probabilities ◽  
Choice Set

A key feature in estimating and applying destination choice models with aggregate alternatives is to sample a set of nonchosen traffic analysis zones (TAZs), plus the one a trip maker chose, to construct a destination choice set. Computational complexity is reduced because the choice set would be too large if all study area TAZs were included in the calibration. Commonly, two types of sampling strategies are applied to draw subsets of alternatives from the universal choice set. The first, and simplest, approach is to select randomly a subset of nonchosen alternatives with uniform selection probabilities and then add the chosen alternative if it is not otherwise included. The approach, however, is not an efficient sampling scheme because most alternatives for a given trip maker may have small choice probabilities. The second approach, stratified importance sampling, draws samples with unequal selection probabilities determined on the basis of preliminary estimates of choice probabilities for every alternative in the universal choice set. The stratified sampling method assigns different selection probabilities to alternatives in different strata. Simple random sampling is applied to draw alternatives in each stratum. However, it is unclear how to divide the study area so that destination TAZs may be sampled effectively. The process of and findings from implementing a stratified sampling strategy in selecting alternative TAZs for calibrating aggregate destination choice models in a geographic information system (GIS) environment are described. In this stratified sampling analysis, stratum regions varied by spatial location and employment size in the adjacent area were defined for each study area TAZ. The sampling strategy is more effective than simple random sampling in regard to maximum log likelihood and goodness-of-fit values.

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.

scholarly journals Exploring Route Choice Behaviours Accommodating Stochastic Choice Set Generations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shin-Hyung Cho ◽  
Seung-Young Kho
Keyword(s):  
Radio Frequency Identification ◽  
Choice Model ◽  
Route Choice ◽  
Revealed Preference ◽  
Guidance System ◽  
Model Parameters ◽  
Generation Model ◽  
Choice Behaviour ◽  
Choice Set Generation ◽  
Choice Set

Modelling route choice behaviours are essential in traffic operation and transportation planning. Many studies have focused on route choice behaviour using the stochastic model, and they have tried to construct the heterogeneous route choice model with various types of data. This study aims to develop the route choice model incorporating travellers’ heterogeneity according to the stochastic route choice set. The model is evaluated from the empirical travel data based on a radio frequency identification device (RFID) called dedicated short-range communication (DSRC). The reliability level is defined to explore the travellers’ heterogeneity in the choice set generation model. The heterogeneous K-reliable shortest path- (HK α RSP-) based route choice model is established to incorporate travellers’ heterogeneity in route choice behaviour. The model parameters are estimated for the mixed path-size correction logit (MPSCL) model, considering the overlapping paths and the heterogeneous behaviour in the route choice model. The different behaviours concerning the chosen routes are analysed to interpret the route choice behaviour from revealed preference data by comparing the different coefficients’ magnitude. There are model validation processes to confirm the prediction accuracy according to travel distance. This study discusses the policy implication to introduce the traveller specified route travel guidance system.

EFFECTS OF CHOICE SET SIZE AND ROUTE CHOICE MODELS ON PATH-BASED TRAFFIC ASSIGNMENT

2008 ◽  
Vol 4 (2) ◽  
pp. 117-133 ◽  
Author(s):  
Shlomo Bekhor ◽  
Tomer Toledo ◽  
Joseph N. Prashker
Keyword(s):  
Traffic Assignment ◽  
Route Choice ◽  
Choice Models ◽  
Set Size ◽  
Choice Set
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