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.

Predicting Consumer Choice Set Using Product Association Network and Data Analytics

2013 ◽  
Author(s):  
Mingxian Wang ◽  
Wei Chen
Keyword(s):  
Engineering Design ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Choice Model ◽  
Choice Modeling ◽  
Large Set ◽  
Association Network ◽  
Choice Sets ◽  
Choice Set ◽  
Product Association

Although discrete choice analysis has been shown to be useful for modeling consumer preferences and choice behaviors in the field of engineering design, information of choice set composition is often not available in majority of the collected consumer purchase data. When a large set of choice alternatives exist for a product, such as automotive vehicles, randomly choosing a small set of product alternatives to form a choice set for each individual consumer will result in misleading choice modeling results. In this work, we propose a data-analytics approach to mine existing data of choice sets and predict the choice set for each individual customer in a new choice modeling scenario where the choice set information is lacking. The proposed data-analytics approach integrates product association analysis, network analysis, consumer segmentation, and predictive analytics. Using the J.D. Power vehicle survey as the existing choice set data, we demonstrate that the association network approach is capable of recognizing and expressively summarizing meaningful product relations in choice sets. Our method accounts for consumer heterogeneity using the stochastic generation algorithm where the probability of selecting an alternative into a choice set integrates the information of customer profile clusters and products chosen frequencies. By comparing multiple multinomial logit models using different choice set compositions, we show that the choice model estimates are sensitive to the choice set compositions and our proposed method leads to improved modeling results. Our method also provides insights into market segmentation that can guide engineering design decisions.

scholarly journals META-ANALYSIS OF CHOICE SET GENERATION EFFECTS ON ROUTE CHOICE MODEL ESTIMATES AND PREDICTIONS

Transport ◽  
2012 ◽  
Vol 27 (3) ◽  
pp. 286-298 ◽  
Author(s):  
Carlo Giacomo Prato
Keyword(s):  
Large Scale ◽  
Choice Model ◽  
Meta Analysis ◽  
Route Choice ◽  
Behavior Modeling ◽  
Path Generation ◽  
Choice Sets ◽  
True Model ◽  
Choice Set Generation ◽  
Choice Set

Large scale applications of behaviorally realistic transport models pose several challenges to transport modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment equilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generate choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper proposes a methodology and an experimental setting to provide general indications about objective judgments for an effective route choice set generation. Initially, path generation techniques are implemented within a synthetic network to generate possible subjective choice sets considered by travelers. Next, ‘true model estimates’ and ‘postulated predicted routes’ are assumed from the simulation of a route choice model. Then, objective choice sets are applied for model estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of models estimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approach allows synthesizing the effect of judgments for the implementation of path generation techniques, since a large number of models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysis estimates suggest that transport modelers should implement stochastic path generation techniques with average variance of its distribution parameters and correction for unequal sampling probabilities of the alternative routes in order to obtain satisfactory results in terms of coverage of ‘postulated chosen routes’, reproduction of ‘true model estimates’ and prediction of ‘postulated predicted routes’.

scholarly journals Measuring the Reliability of Methods and Algorithms for Route Choice Set Generation: Empirical Evidence from a Survey in the Naples Metropolitan Area

2020 ◽  
Vol 14 (1) ◽  
pp. 50-66
Author(s):  
Fulvio Simonelli ◽  
Fiore Tinessa ◽  
Ciro Buonocore ◽  
Francesca Pagliara
Keyword(s):  
Metropolitan Area ◽  
Route Choice ◽  
Network Coverage ◽  
Trade Off ◽  
Network Loading ◽  
Data Collection Process ◽  
Choice Sets ◽  
And Performance ◽  
Choice Set Generation ◽  
Choice Set

Background: Route choice set definition is a very sensitive phase of the route choice simulation. Several heuristics, generally based on shortest path algorithm repetition, give as output choice sets that are very large, lading to questions about their behavioural consistency. Objective: This paper proposes a comparison of the main route choice set generation methods, contrasting the results of the commonly implemented heuristics with the revealed choice sets of a sample of employees and students moving within the Metropolitan Area of Naples. Methods: We described the data collection process and provided a statistical analysis of the sample data. In addition, since coverage measures and performance indicators, usually applied in the literature, do not take into account any possible biases related to the generated choice set cardinality. The current work proposes an analysis of the coverage of routes that are generated by the heuristics towards the revealed routes. Results: We observed that when the heuristics did not provide overlapped routes, although giving higher network coverage, they introduced a higher number of links not belonging to any observed route. In general, this may cause significant network loading errors. Therefore, the quality of a method for choice set generation should be measured as a function of the trade-off amongst network coverage and network loading bias due to excessive cardinality of the generated choice-sets. Conclusion: We found the randomization method, which is also less computational demanding, provided the best trade-off amongst network coverage and network loading bias

A turning point-based offline map matching algorithm for urban road networks

2021 ◽  
Vol 565 ◽  
pp. 32-45
Author(s):  
Dongqing Zhang ◽  
Yucheng Dong ◽  
Zhaoxia Guo
Keyword(s):  
Turning Point ◽  
Road Networks ◽  
Map Matching ◽  
Matching Algorithm ◽  
Urban Road

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 The Effect of Route-choice Strategy on Transit Travel Time Estimates

2019 ◽  
Author(s):  
Nate Wessel ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Imperfect Information ◽  
Optimal Path ◽  
Shortest Paths ◽  
Route Choice ◽  
Travel Times ◽  
Selection Strategies ◽  
Time Estimates ◽  
Average Travel Time

Estimates of travel time by public transit often rely on the calculation of a shortest-path between two points for a given departure time. Such shortest-paths are time-dependent and not always stable from one moment to the next. Given that actual transit passengers necessarily have imperfect information about the system, their route selection strategies are heuristic and cannot be expected to achieve optimal travel times for all possible departures. Thus an algorithm that returns optimal travel times at all moments will tend to underestimate real travel times all else being equal. While several researchers have noted this issue none have yet measured the extent of the problem. This study observes and measures this effect by contrasting two alternative heuristic routing strategies to a standard shortest-path calculation. The Toronto Transit Commission is used as a case study and we model actual transit operations for the agency over the course of a normal week with archived AVL data transformed into a retrospective GTFS dataset. Travel times are estimated using two alternative route-choice assumptions: 1) habitual selection of the itinerary with the best average travel time and 2) dynamic choice of the next-departing route in a predefined choice set. It is shown that most trips present passengers with a complex choice among competing itineraries and that the choice of itinerary at any given moment of departure may entail substantial travel time risk relative to the optimal outcome. In the context of accessibility modelling, where travel times are typically considered as a distribution, the optimal path method is observed in aggregate to underestimate travel time by about 3-4 minutes at the median and 6-7 minutes at the \nth{90} percentile for a typical trip.

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.

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