Journal articles: 'Route choice models'

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Daamen, Winnie, Piet H. L. Bovy, and Serge P. Hoogendoorn. "Influence of Changes in Level on Passenger Route Choice in Railway Stations." Transportation Research Record: Journal of the Transportation Research Board 1930, no. 1 (January 2005): 12–20. http://dx.doi.org/10.1177/0361198105193000102.

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In assessing the design of a public transfer station, it is important to be able to predict the routes taken by passengers. Most simulation tools use simple route choice models that take into account only the shortest walking distance or walking time between a passenger's origin and destination. To improve this type of route choice model, other factors affecting passenger route choice need to be identified. Also, the way these factors influence route choice behavior needs to be determined to indicate how each factor is valued. In this research, route choice data have been collected in two Dutch train stations by following passengers through the facility from their origins to their destinations. These data have been used to estimate extended route choice models. The focus is on the influences of level changes in walking routes on passenger route choice behavior. It appears that ways of bridging level changes (ramps, stairs, escalators) each have a significant and different impact on the attractiveness of a route to a traveler.

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Pandey, Venktesh, and Stephen D. Boyles. "Comparing Route Choice Models for Managed Lane Networks with Multiple Entrances and Exits." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (May 20, 2019): 381–93. http://dx.doi.org/10.1177/0361198119848706.

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Developing an appropriate route choice model for managed lanes with multiple entrances and exits is critical for the success of managed lane planning and operations. This research focuses on route choice models for managed lane networks with stochastic and time-varying tolls and travel times. In the model, a traveler receives real-time information about the tolls and travel times upon arrival at each diverge node and makes a dynamic lane choice decision that minimizes the total expected cost. The online route choice model is formulated as a Markov decision process and solved using a backward recursion algorithm. The model is compared against four other routing models: a binary logit model, a model based on decision routes, a model that chooses paths a priori, and a model with routes chosen randomly. The study also models irrational driver behavior with parameters like driver’s inclination toward making optimal lane choices and preference for certain lanes. Findings show that the expected costs from the routes chosen using the decision route model from the literature are close to the optimal cost with an average percentage error of 0.93%. The binary logit model is shown to have a high average error of 50% in the expected cost when a driver is assumed to behave rationally, but the same model shows optimal prediction for certain irrational driver behaviors. The proposed routing model forms a basis for future work in the area of managed lane pricing and planning.

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Alivand, Majid, Hartwig Hochmair, and Sivaramakrishnan Srinivasan. "Analyzing how travelers choose scenic routes using route choice models." Computers, Environment and Urban Systems 50 (March 2015): 41–52. http://dx.doi.org/10.1016/j.compenvurbsys.2014.10.004.

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Prato, Carlo Giacomo. "META-ANALYSIS OF CHOICE SET GENERATION EFFECTS ON ROUTE CHOICE MODEL ESTIMATES AND PREDICTIONS." TRANSPORT 27, no. 3 (September 19, 2012): 286–98. http://dx.doi.org/10.3846/16484142.2012.719840.

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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’.

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Zhu, Shanjiang, and David Levinson. "Agent-Based Route Choice with Learning and Exchange of Information." Urban Science 2, no. 3 (July 15, 2018): 58. http://dx.doi.org/10.3390/urbansci2030058.

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Planning models require consideration of travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g., willingness to switch routes with potential savings) in a differentiated market (where routes have varying tolls and levels of service). This paper proposes to explicitly model the formation and spreading of spatial knowledge among travelers, following cognitive map theory. An agent-based route choice (ARC) model was developed to track choices of each individual decision-maker in a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied to both the Sioux Falls and Chicago sketch networks. Comparisons between ARC and existing models (user equilibrium (UE) and stochastic user equilibrium (SUE)) on both networks show ARC is valid and computationally tractable. In brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of transportation planning under an integrated framework.

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Prashker, J. N., and S. Bekhor. "Investigation of Stochastic Network Loading Procedures." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (January 1998): 94–102. http://dx.doi.org/10.3141/1645-12.

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The network loading process of stochastic traffic assignment is investigated. A central issue in the assignment problem is the behavioral assumption governing route choice, which concerns the definition of available routes and the choice model. These two problems are addressed and reviewed. Although the multinomial logit model can be implemented efficiently in stochastic network loading algorithms, the model suffers from theoretical drawbacks, some of them arising from the independence of irrelevant alternatives property. As a result, the stochastic loading on routes that share common links is overloaded at the overlapping parts of the routes. Other logit-family models recently have been proposed to overcome some of the theoretical problems while maintaining the convenient analytical structure. Three such models are investigated: the C-logit model, which was specifically defined for route choice; and two general discrete-choice models, the cross-nested logit model and the paired combinatorial logit model. The two latter models are adapted to route choice, and simple network examples are presented to illustrate the performance of the models with respect to the overlapping problem. The results indicate that all three models perform better than does the multinomial logit model. The cross-nested logit model has an advantage over the two other generalized models because it enables performing stochastic loading without route enumeration. The integration of this model with the stochastic equilibrium problem is discussed, and a specific algorithm using the cross-nest logit model is presented for the stochastic loading phase.

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Shakeel, Kiran, Taha Hossein Rashidi, and Travis S. Waller. "Choice Set Formation Behavior in Selecting Travel Routes: Application of an Interactive Online Survey Platform." Transportation Research Record: Journal of the Transportation Research Board 2669, no. 1 (January 2017): 1–9. http://dx.doi.org/10.3141/2669-01.

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One of the major challenges associated with the analysis of route choice modeling is the formulation of the choice set of alternatives that may allow a relatively accurate prediction of demand for travel routes. The subset of route alternatives in the choice set should be relevant and feasible and include the attributes considered most by travelers when they choose a route. This research investigated the role and significance of route choice set formations with a focus on the perspectives of the modeler and of travelers. Revealed preference data were collected from Sydney, Australia, residents about their choice of route for their most recent commuting trip. The survey tool was programmed to use the Google Maps application programming interfaces to collect the route choice information, including the selected route and the set of routes that were considered. Three discrete choice models were used to investigate the traveler’s inclination toward certain attributes of routes, considering both car and public transit routes with the master choice set. The effect of possible bias generated because of the formation of route choice from the perspective of the modeler was also analyzed and presented with the results. The results show the intuitive signs of various attributes, with travel time being the significant factor for route choice. The difference between the choice sets considered by the traveler and by the modeler also suggests that those considered by the modeler possess enough variation to offer the possibility of better capturing important factors affecting route choice behavior.

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Yai, Tetsuo, and Tetsuo Shimizu. "Multinomial Probit with Structured Covariance for Choice Situations with Similar Alternatives." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (January 1998): 69–75. http://dx.doi.org/10.3141/1645-09.

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The estimatability of the multinomial probit (MNP) model has been improved greatly by the modeling of the error structure. A modeling idea for travelers’ choice behaviors using the MNP with structured covariances (MNPSC) model is proposed. Choice of route, destination, and time of day are considered. The covariance of the error term of the utility function is structured by introducing the overlapped length between any two routes and the overlapped time length between any two departure times. Simultaneous models such as route and train class, route and mode, and time of day and mode choice model also are introduced by applying the MNPSC modeling idea. These are formulated easily by assuming that the error is generated independently between two choices. An empirical study for a route and train class choice model was conducted by using the passenger survey data in the Tokyo metropolitan region, and its estimatability and applicability are examined.

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Tribby, Calvin P., Harvey J. Miller, Barbara B. Brown, Carol M. Werner, and Ken R. Smith. "Analyzing walking route choice through built environments using random forests and discrete choice techniques." Environment and Planning B: Urban Analytics and City Science 44, no. 6 (July 20, 2016): 1145–67. http://dx.doi.org/10.1177/0265813516659286.

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Walking is a form of active transportation with numerous benefits, including better health outcomes, lower environmental impacts and stronger communities. Understanding built environmental associations with walking behavior is a key step towards identifying design features that support walking. Human mobility data available through GPS receivers and cell phones, combined with high resolution walkability data, provide a rich source of georeferenced data for analyzing environmental associations with walking behavior. However, traditional techniques such as route choice models have difficulty with highly dimensioned data. This paper develops a novel combination of a data-driven technique with route choice modeling for leveraging walkability audits. Using data from a study in Salt Lake City, UT, USA, we apply the data-driven technique of random forests to select variables for use in walking route choice models. We estimate data-driven route choice models and theory-driven models based on predefined walkability dimensions. Results indicate that the random forest technique selects variables that dramatically improve goodness of fit of walking route choice models relative to models based on predefined walkability dimensions. We compare the theory-driven and data-driven walking route choice models based on interpretability and policy relevance.

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Bekhor, Shlomo, Moshe E. Ben-Akiva, and M. Scott Ramming. "Evaluation of choice set generation algorithms for route choice models." Annals of Operations Research 144, no. 1 (April 2006): 235–47. http://dx.doi.org/10.1007/s10479-006-0009-8.

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Misra, Aditi, and Kari Watkins. "Modeling Cyclist Route Choice using Revealed Preference Data: An Age and Gender Perspective." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 3 (November 9, 2018): 145–54. http://dx.doi.org/10.1177/0361198118798968.

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Perception of safety varies among cyclists depending on their cycling experience and frequency as well as on their socio-demographic attributes, particularly age and gender. Female cyclists are often more likely to feel unsafe than their male counterparts under similar traffic conditions or surrounding environment. In general, female cyclists are known to avoid slopes and high traffic, and prefer shorter trips than their male counterparts. However, there is no research that has yet explicitly modeled the differences in route choice between female and male cyclists using revealed preference data. In this research, we use GPS enabled smartphone based route data to develop segmented route choice models for different age groups and genders. The results indicate that segmented models are significantly better representatives of route choices made by female and male cyclists than pooled models. The findings suggest that traffic characteristics such as speed and annual average daily traffic (AADT) influence the decision to choose a particular route differently for female cyclists and male cyclists. Similar results are seen across different age groups as well. Preference towards routes with cycling facilities are however universal and no significant difference is noted among gender or age segments in this regard.

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Croce, Antonello Ignazio, Giuseppe Musolino, Corrado Rindone, and Antonino Vitetta. "Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data." Sustainability 12, no. 20 (October 16, 2020): 8557. http://dx.doi.org/10.3390/su12208557.

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According to the literature, the path choice decision process of a user of a (road) transport network, named path choice problem (PCP), is composed of two levels/models: the definition of perceived alternative paths (choice set) and the choice of one path in the path choice set. The path choice probability can be estimated with two models: a choice model of the path choice set and a choice model of a path (Mansky paradigm). In this research, the paper’s contribution concerns two elements: extension of the PCP paradigm (two-level models) consolidated in the literature to the route choice decision process (vehicle routing problem (VRP)) and identification of common elements in the PCP and VRP concerning the criteria in the two decision levels and the procedure for route and path selection and choice. The experiment concerns the comparison of observed routes with simulated and optimized routes of commercial vehicles to analyse the level of similarity and coverage. The observed routes are extracted from floating car data (FCD) from commercial vehicles travelling inside a study area inside the Calabria Region (Southern Italy). The comparison is executed in terms of similarity of the sequences of nodes visited between observed routes and simulated/optimized routes.

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Ho, Javier, and Paul Bernal. "UNDERSTANDING THE THERMAL COAL MOVEMENTS FROM COLOMBIA TO CHILE THROUGH THE PANAMA CANAL USING LOGIT MODELS- LOOKING AHEAD." Journal of Maritime Logistics 2, no. 1 (August 31, 2022): 1–29. http://dx.doi.org/10.46754/jml.2022.08.001.

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This study attempted to specify logit models for bulkers transporting mostly thermal coal from the East Coast of Colombia to Chile through the Panama Canal compared to the alternative route. The preliminary proposed predictors for the logit models included voyage cost variables and Canal's attributes. For the route choice of coal from the East Coast of Colombia to Chile, voyage cost factors such as Panama Canal cost, distance difference between Panama versus alternative route, post arrival of vessel to the next port and the maximum transit draft were important factors in this choice, as well as Panama Canal attributes such as vessel arrivals at the Panama Canal and the Panamax Plus requirement to transit the neopanamax locks. The route choice involved the Panama Canal and Cape Horn/Magellan Strait in the Southern tip of South America. This study analyzed coal traffic between October 1, 2016, and September 30, 2020, and briefly discussed the future of coal movements through Panama, given Chile's long term plans to generate electricity using renewanable energy sources and hydrogen. This paper is a contribution to the discrete choice literature and attempted to provide insights into route choice factors involving the Panama Canal, proposing new preliminary explanatory variables to better understand route choices that may apply in future Panama Canal studies. The study will be a contribution to the universal maritime coal transportation literature, and it is a continuation on research related to the Panama canal, particularly on route choices using AIS information.

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Dhulipala, Sowjanya. "Multi-route choice modelling in a metropolitan context: A comparative analysis using Multinomial Logit and Fuzzy Logic based approaches." European Transport/Trasporti Europei 79, ET.2020 (September 2020): 1–17. http://dx.doi.org/10.48295/et.2020.79.4.

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Route choice plays a vital role in the traffic assignment and network building, as it involves decision making on part of riders. The vagueness in travellers’ perceptions of attributes of the available routes between any two locations adds to the complexities in modelling the route choice behaviour. Conventional Logit models fail to address the uncertainty in travellers’ perceptions of route characteristics (especially qualitative attributes, such as environmental effects), which can be better addressed through the theory of fuzzy sets and linguistic variables. This study thus attempts to model travellers’ route choice behaviour, using a fuzzy logic approach that is based on simple and logical ‘if-then’ linguistic rules. This approach takes into consideration the uncertainty in travellers’ perceptions of route characteristics, resembling humans’ decision-making process. Three attributes – travel time, traffic congestion, and road-side environment are adopted as factors driving people’s choice of routes, and three alternative routes between two typical locations in an Indian metropolitan city, Surat, are considered in the study. The approach to deal with multiple routes is shown by analyzing two-wheeler riders’ (e.g. motorcyclists’ and scooter drivers’) route choice behaviour during the peak-traffic time. Further, a Multinomial Logit (MNL) model is estimated, to enable a comparison of the two modelling approaches. The estimated Fuzzy Rule-Based Route Choice Model outperformed the conventional MNL model, accounting for the uncertain behaviour of travellers.

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Yao, Rui, and Shlomo Bekhor. "Data-driven choice set generation and estimation of route choice models." Transportation Research Part C: Emerging Technologies 121 (December 2020): 102832. http://dx.doi.org/10.1016/j.trc.2020.102832.

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Sasai, Syuya, Tetsuro Hyodo, Norikazu Suzuki, and Yoji Takahashi. "A Comparison Analysis on Bicycle Route Choice Models." INFRASTRUCTURE PLANNING REVIEW 21 (2004): 597–606. http://dx.doi.org/10.2208/journalip.21.597.

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KOBAYASHI, Kiyoshi, and Katsumi FUJITAKA. "Route choice models with endogenous rational expectations formation." Doboku Gakkai Ronbunshu, no. 458 (1993): 17–26. http://dx.doi.org/10.2208/jscej.1993.17.

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Frejinger, E., and M. Bierlaire. "Capturing correlation with subnetworks in route choice models." Transportation Research Part B: Methodological 41, no. 3 (March 2007): 363–78. http://dx.doi.org/10.1016/j.trb.2006.06.003.

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Yichao, Pu. "Verification and Optimization of Metro Fare Clearing Models Based on Travel Route Reconstruction." New Metro 1, no. 1 (December 2, 2020): 34–47. http://dx.doi.org/10.37819/nm.001.01.0076.

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How to verify and optimize metro fare clearing models efficiently and accurately is a research focus in metro operations. Metro fare clearing models are mostly based on probability distributions. In such models, the normal distribution of travel time corresponding to the section probabilities is used to calculate the route choice probabilities of passengers on a multi-route metro network. By integrating the operating mileage proportions of each metro line operator and the corresponding route choice probabilities, the fare clearing proportions are calculated for all the operators of the metro network. To verify the accuracy of the fare clearing proportions, we propose a travel route reconstruction approach based on cell phone data acquisition technique. With wireless access point (AP) sensors installed at transfer stations, the unique medium access control (MAC) address of the smart phone with Wi-Fi function turned on is recorded and transmitted to a data analysis platform. After matching the MAC address information with time and location, the travel route of the smart phone user is reconstructed. Then, the parameters in the fare clearing model are verified and optimized according to the travel route choice probabilities. The proposed methodology is applied in Hangzhou metro network for experiment, and the metro fare clearing model is verified and modified by reconstructing the actual travel routs of the local passengers.

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Tatineni, Maya, David E. Boyce, and Pitu Mirchandani. "Comparisons of Deterministic and Stochastic Traffic Loading Models." Transportation Research Record: Journal of the Transportation Research Board 1607, no. 1 (January 1997): 16–23. http://dx.doi.org/10.3141/1607-03.

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Stochastic route choice models have been proposed as a more realistic alternative to the simpler deterministic route choice representations in models for loading traffic onto a network. However, studies on stochastic route choice models have thus far been limited to small experimental networks. Because stochastic models are in general considered to be more realistic in their assumptions than deterministic models, it becomes pertinent to examine their implementation and the corresponding traffic loading on a network of realistic size. Such an implementation for two stochastic route choice models is examined. The results indicate that the differences among the models, at the network level, for predicted link flows and average travel times between origin and destinations are not major, and therefore, deterministic models may suffice for long-range network planning. However, stochastic models predict the usage of more links in the network, and the difference in the flows at the link level may play an important role in travel advisory and route guidance systems. Some suggestions for future research are presented.

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Lima, Antonio, Rade Stanojevic, Dina Papagiannaki, Pablo Rodriguez, and Marta C. González. "Understanding individual routing behaviour." Journal of The Royal Society Interface 13, no. 116 (March 2016): 20160021. http://dx.doi.org/10.1098/rsif.2016.0021.

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Knowing how individuals move between places is fundamental to advance our understanding of human mobility (González et al . 2008 Nature 453, 779–782. ( doi:10.1038/nature06958 )), improve our urban infrastructure (Prato 2009 J. Choice Model. 2, 65–100. ( doi:10.1016/S1755-5345(13)70005-8 )) and drive the development of transportation systems. Current route-choice models that are used in transportation planning are based on the widely accepted assumption that people follow the minimum cost path (Wardrop 1952 Proc. Inst. Civ. Eng. 1, 325–362. ( doi:10.1680/ipeds.1952.11362 )), despite little empirical support. Fine-grained location traces collected by smart devices give us today an unprecedented opportunity to learn how citizens organize their travel plans into a set of routes, and how similar behaviour patterns emerge among distinct individual choices. Here we study 92 419 anonymized GPS trajectories describing the movement of personal cars over an 18-month period. We group user trips by origin–destination and we find that most drivers use a small number of routes for their routine journeys, and tend to have a preferred route for frequent trips. In contrast to the cost minimization assumption, we also find that a significant fraction of drivers' routes are not optimal. We present a spatial probability distribution that bounds the route selection space within an ellipse, having the origin and the destination as focal points, characterized by high eccentricity independent of the scale. While individual routing choices are not captured by path optimization, their spatial bounds are similar, even for trips performed by distinct individuals and at various scales. These basic discoveries can inform realistic route-choice models that are not based on optimization, having an impact on several applications, such as infrastructure planning, routing recommendation systems and new mobility solutions.

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ALI, Yousaf, and Muhammad SABIR. "MODE-ROUTE CHOICE DECISIONS: A CASE STUDY OF CPEC INVESTMENT IN PAKISTAN RAILWAYS." Scientific Journal of Silesian University of Technology. Series Transport 115 (June 30, 2022): 5–21. http://dx.doi.org/10.20858/sjsutst.2022.115.1.

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This study proposes the use of multi-criteria decision models (MCDM) for transportation mode-route choice decisions. This method is beneficial when trips' microdata are unavailable. Route-mode choice decisions were investigated for three public transportation modes (buses, railways, and airlines) in the post-China Pakistan Economic Corridor (CPEC) investment in Pakistan Railways (PR) for a link between Peshawar and Karachi. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) was used for the mode choice decisions and a hybrid model of AHP (Analytical Hierarchy Approach) – TOPSIS was used for the route choice decision ML-1 link of PR. This study concludes that rails were the best mode of transportation in post-CPEC investment. Furthermore, route 3, linking Karachi to Peshawar via Lodhran, Multan, and Miniawali, is the best route connection among the four considered routes.

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Gao, Song, Emma Frejinger, and Moshe Ben-Akiva. "Adaptive Route Choice Models in Stochastic Time-Dependent Networks." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (January 2008): 136–43. http://dx.doi.org/10.3141/2085-15.

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Adaptive route choice models are studied that explicitly capture travelers’ route choice adjustments according to information on realized network conditions in stochastic time-dependent networks. Two types of adaptive route choice models are explored: an adaptive path model in which a sequence of path choice models are applied at intermediate decision nodes and a routing policy choice model in which the alternatives correspond to routing policies rather than paths at the origin. A routing policy in this study is a decision rule that maps from all possible pairs (e.g., node, time) to the next links out of the node. Existing route choice models that can be estimated on disaggregate revealed preferences assume a deterministic network setting from the traveler's perspective and cannot capture the traveler's proactive adaptive behavior under uncertain traffic conditions. The literature includes a number of algorithmic studies of optimal routing policy problems, but the estimation of a routing policy choice model is a new research area. The specifications of estimating the two adaptive route choice models are established and the feasibility of estimation from path observations is demonstrated on an illustrative network. Prediction results from three models–nonadaptive path model, adaptive path model, and routing policy model–are compared. The routing policy model is shown to better capture the option value of diversion than the adaptive path model. The difference between the two adaptive models and the nonadaptive model is larger in terms of expected travel time if the network is more stochastic, indicating that the benefit of adaptivity is more significant in a more unpredictable network.

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Broach, Joseph, and Jennifer Dill. "Using Predicted Bicyclist and Pedestrian Route Choice to Enhance Mode Choice Models." Transportation Research Record: Journal of the Transportation Research Board 2564, no. 1 (January 2016): 52–59. http://dx.doi.org/10.3141/2564-06.

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Vovsha, Peter, and Shlomo Bekhor. "Link-Nested Logit Model of Route Choice: Overcoming Route Overlapping Problem." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (January 1998): 133–42. http://dx.doi.org/10.3141/1645-17.

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A new link-nested logit model of route choice is presented. The model is derived as a particular case of the generalized-extreme-value class of discrete choice models. The model has a flexible correlation structure that allows for overcoming the route overlapping problem. The corresponding stochastic user equilibrium is formulated in two equivalent mathematical programming forms: as a particular case of the general Sheffi formulation and as a generalization of the logit-based Fisk formulation. A stochastic network loading procedure is proposed that obviates route enumeration. The proposed model is then compared with alternative assignment models by using numerical examples.

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Yamamoto, Toshiyuki, Ryuichi Kitamura, and Junichiro Fujii. "Drivers’ Route Choice Behavior: Analysis by Data Mining Algorithms." Transportation Research Record: Journal of the Transportation Research Board 1807, no. 1 (January 2002): 59–66. http://dx.doi.org/10.3141/1807-08.

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Decision trees and production rules, which are among the methods used in knowledge discovery and data mining, are applied to investigate drivers’ route choice behavior. These methods have an advantage over artificial neural networks, another data mining method often used in analysis of travel behavior: they facilitate determination of the relationships between the explanatory variables and the choice. Specifically, the C4.5 algorithm, which produces a decision tree and a set of production rules from the tree, is applied here. Two surveys were carried out to collect data on drivers’ route choice behavior between two alternative routes on expressway networks. The two data sets include the expected minimum, maximum, and average travel times along each alternative route, as indicated by the respondent as well as his or her sociodemographic attributes. The results of the analyses suggest that different expected travel times influence route choice in different cases and that a maximum or average travel time determines route choice in some cases regardless of other attributes. The results of a comparison analysis between the C4.5 algorithm and discrete choice models indicate the superior ability offered by the former in representing drivers’ route choice.

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Hoogendoorn, S. P., and P. H. L. Bovy. "Pedestrian route-choice and activity scheduling theory and models." Transportation Research Part B: Methodological 38, no. 2 (February 2004): 169–90. http://dx.doi.org/10.1016/s0191-2615(03)00007-9.

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Quattrone, Agata, and Antonino Vitetta. "Random and fuzzy utility models for road route choice." Transportation Research Part E: Logistics and Transportation Review 47, no. 6 (November 2011): 1126–39. http://dx.doi.org/10.1016/j.tre.2011.04.007.

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Knapen, Luk, Irith Ben-Arroyo Hartman, and Tom Bellemans. "Using path decomposition enumeration to enhance route choice models." Future Generation Computer Systems 107 (June 2020): 1077–88. http://dx.doi.org/10.1016/j.future.2017.12.053.

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Mai, Tien, Emma Frejinger, and Fabian Bastin. "A misspecification test for logit based route choice models." Economics of Transportation 4, no. 4 (December 2015): 215–26. http://dx.doi.org/10.1016/j.ecotra.2015.08.002.

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Politis, Ioannis, Michalis Kyriakoglou, Georgios Georgiadis, and Panagiotis Papaioannou. "Evidence from Highway Drivers in Greece Showing Toll Avoidance and Utility of Alternative Routes." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 8, 2020): 948–58. http://dx.doi.org/10.1177/0361198120933265.

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Road tolling plays a significant role on highways’ financial sustainability since it consists the major revenue source. This paper aims to examine the factors that affect the drivers’ route choice and urge them to avoid toll roads when an alternative toll-free route is available. The paper presents the results of a case study that is dealing with the issue of toll avoidance at the last non-privatized highway of Greece, the Egnatia Odos (EO) road. Data from a combined revealed and stated preference survey were collected and binary choice models were built for car and truck drivers so as to determine the utility of alternative routes. The results show that travel cost and toll fees are critical route choice criteria for car drivers, while travel time is a key decision factor for truck drivers. The high safety standards for the toll route were appreciated by both categories of drivers. Additional trip and personal characteristics, such as gender, trip frequency, type of transported cargo, and total trip length also affect drivers’ choices. The elasticity of travel time and cost was estimated to shed light on drivers’ sensitiveness to the route attributes and it was found that truck drivers’ choices are greatly influenced by their working time schedules. These findings highlight the key factors that influence the utility of toll roads and therefore could assist highway authorities and concessionaires in developing successful toll pricing policies which will not act as a deterrent to the use of highways.

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Avineri, Erel, and Joseph N. Prashker. "Sensitivity to Uncertainty: Need for a Paradigm Shift." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 90–98. http://dx.doi.org/10.3141/1854-10.

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Existing common route choice models are based on random utility theory, which follows the maximum utility assumption. Recent intelligent transportation system applications have highlighted the need for better models of the behavioral processes involved in route choice decisions. Therefore, prediction of travelers' responses to uncertainty was analyzed. Route choice experiments were conducted to evaluate the effect of the feedback mechanism on decision making under uncertainty. The experimental results were compared with those from a model based on cumulative prospect theory and models based on learning approaches. It is shown that a traveler's sensitivity to travel time differences is lower when variances in travel times are higher. This better understanding of route choice behavior predicted by learning models may improve traffic predictions, as well as the design of traffic control mechanisms.

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Vacca, Alessandro, Carlo Giacomo Prato, and Italo Meloni. "Estimating Route Choice Models from Stochastically Generated Choice Sets on Large-Scale Networks." Transportation Research Record: Journal of the Transportation Research Board 2493, no. 1 (January 2015): 11–18. http://dx.doi.org/10.3141/2493-02.

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Chen, Anthony, Maya Tatineni, Der-Horng Lee, and Hai Yang. "Effect of Route Choice Models on Estimating Network Capacity Reliability." Transportation Research Record: Journal of the Transportation Research Board 1733, no. 1 (January 2000): 63–70. http://dx.doi.org/10.3141/1733-09.

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The issue of planning for adequate capacity in transportation systems to accommodate growing traffic demand is becoming a serious problem. Recent research has introduced "capacity reliability" as a new network performance index. Capacity reliability is defined as the probability that a network can accommodate a certain volume of traffic demand at a required service level given variable arc capacities, while accounting for drivers' route choice behavior. Previous papers on this topic provide a comprehensive methodology for assessing capacity reliability along with extensive simulation results. However, an important issue that remains is what type of route choice model should be used to model driver behavior in estimating network capacity reliability. Three different route choice models (one deterministic and two stochastic models) are compared, and the effect of using each of these models on estimating network capacity reliability is examined.

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Liu, Sha Sha, En Jian Yao, Yong Sheng Zhang, and Ling Lu. "Study on Route Choice Behavior of Urban Rail Transit Passengers." Applied Mechanics and Materials 587-589 (July 2014): 2252–56. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.2252.

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In order to capture spatiotemporal distribution pattern of passenger flow under networked condition, it is necessary to analyze route choice behavior of urban rail transit passengers. First, angular cost value and comfort index are defined to reflect the influence of network structures, route directions and in-vehicle congestion on passengers’ route choice behavior respectively; Then, two route choice models are proposed respectively for peak and off-peak hours, in which new variables including angular cost value, comfort index and personal characteristics, as well as level of service variables (i.e. in-vehicle travel time, number of transfers and transfer time etc. , which are usually found in the base model) are considered. Finally, the models are calibrated with the surveyed data from Guangzhou Metro and compared with each other. The results show that the new variables significantly improve models’ explanatory and predictive abilities on route choice behavior of urban rail transit passengers.

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Wang, Jianqiang, and Shiwei Li. "Modeling and simulation of network traffic flow evolution based on incomplete information feedback strategies in the ATIS environment." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 03 (September 2017): 1750031. http://dx.doi.org/10.1142/s1793962317500313.

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Considering both the high complexity of urban traffic flow systems and the bounded rationality of travelers, providing traffic information to all travelers is an effective method to induce each individual to make a more rational route-choice decision. Within Advanced Traveler Information System (ATIS) working environment, temporal and spatial evolution processes of traffic flow in urban road networks are closely related to strategies of providing traffic information and contents of information. In view of the day-to-day route-choice situations, this study constructs original updating models of the cognitive travel time of travelers under four conditions, including not providing any route travel time, only providing the most rapid route travel time, only providing the most congested route travel time, and providing all the routes travel times. The disaggregate route-choice approach is adopted for simulation to reveal the relationship between the evolution process of network traffic flow and the strategy of providing traffic information. The simulation shows that providing traffic information to all travelers cannot improve the operational efficiency of road networks. It is noteworthy that an inappropriate information feedback strategy would lead to intense variation in various routes traffic flow. Compared with incomplete information feedback strategies, it is inefficient and superfluous to provide complete traffic information to all travelers.

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Bogers, Enide A. I., Francesco Viti, and Serge P. Hoogendoorn. "Joint Modeling of Advanced Travel Information Service, Habit, and Learning Impacts on Route Choice by Laboratory Simulator Experiments." Transportation Research Record: Journal of the Transportation Research Board 1926, no. 1 (January 2005): 189–97. http://dx.doi.org/10.1177/0361198105192600122.

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A conceptual modeling framework is proposed, and mathematical submodels for route choice on motorways and urban networks are derived. The models convey the most relevant aspects that play a role in route choice, including learning, risk attitude under uncertainty, habit, and the impacts of advanced travel information service on route choice and learning. To gain insight into the relative importance of the different aspects and processes of route choice behavior, which support the proposed conceptual framework, the models were estimated with data from two experiments carried out with a so-called interactive travel simulator. The latter is a new research laboratory that combines the advantages of both stated preference and revealed preference research. Many relevant contributions on the aforementioned aspects that play a role in route choice can be found in the literature, but a simultaneous consideration of all is lacking. On the basis of these contributions from the literature, a conceptual framework that integrates these aspects was developed. The results from the laboratory experiments indicate that people perform best under the most elaborate information scenario and that habit and inertia together with en route information play a major role in route choice. Learning about route attributes is especially important during the first days but then plays a smaller role than the provided information and the developed habit. Finally, the way information is presented has a great impact on route choice.

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Jensen, Anders F., Thomas K. Rasmussen, and Carlo G. Prato. "A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles." Sustainability 12, no. 3 (February 5, 2020): 1149. http://dx.doi.org/10.3390/su12031149.

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Battery Electric Vehicles (BEVs) play an important role in the needed transition away from fossil fuels and Internal Combustion Engine Vehicles (ICEVs). Although transport planning models and routing problem solutions exist for BEVs, the assumption that BEV drivers search for the shortest path while constraining energy consumption does not have any empirical basis. This study presents a study of actual route choice behavior of drivers from 107 Danish households participating in a large-scale experiment with BEVs and at the same time driving their ICEVs. GPS traces from 8968 BEV and 6678 ICEV routes were map matched to a detailed road network to construct observed routes, and a route choice model was specified and estimated to capture behavioral differences related to the vehicle type. The results reveal that drivers had a higher sensitivity to travel time and trip length when driving BEVs, and to route directness after receiving the BEV, regardless of vehicle type. The results suggest the need to revise the assumptions of transport planning models and routing problems for BEVs in order not to fail to predict what drivers will do by ignoring differences and similarities related to vehicle type.

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Muraleetharan, Thambiah, Kunio Meguro, Takeo Adachi, Toru Hagiwara, and Sei'ichi Kagaya. "Influence of Winter Road Conditions and Signal Delay on Pedestrian Route Choice in Japan's Snowiest Metropolis." Transportation Research Record: Journal of the Transportation Research Board 1939, no. 1 (January 2005): 145–53. http://dx.doi.org/10.1177/0361198105193900117.

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Investigation of pedestrian route choice behavior on icy surfaces is important for the effective improvement of walkways in winter. The objective of this research was to investigate pedestrian route choice behavior in winter. Field surveys and questionnaire surveys were conducted to fulfill this objective. Video cameras were used in the field surveys to clarify the movements of pedestrians. How pedestrians chose their routes was investigated by observing their movements. According to the field survey, when the signal was green, the probability that the pedestrian would cross became extremely high, regardless of the road surface conditions. However, when the walkway surface was icy, the probability that the pedestrian would wait for a green signal decreased by a considerable value. This indicates that when the wait becomes long, the probability that the pedestrian will cross becomes low during the snowy season. A questionnaire survey was also conducted to clarify the factors affecting pedestrian route choice behavior. The questionnaire asked about different road surface conditions. The results from the survey indicate that even if part of a road section has a good surface condition, it has a strong influence on route choice behavior. It indicates that pedestrians feel uncomfortable in walking on slippery walkways and they prefer to choose bare walkways. On the basis of the data from the field survey and questionnaire survey, logit models were developed to express quantitatively the route choice behaviors of pedestrians. These models can be used to predict the probability that a pedestrian will select a route as a function of pedestrian delay at signalized intersections and the road surface conditions in winter.

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Zhao, Lei, Hongzhi Guan, Junze Zhu, and Yunfeng Wei. "A Reliability-Based Network Equilibrium Model with Adaptive Risk-Averse Travelers." Journal of Advanced Transportation 2018 (December 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/5294185.

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In this paper, route free-flow travel time is taken as the lower bound of route travel time to examine its impacts on budget time and reliability for degradable transportation networks. A truncated probability density distribution with respect to route travel time is proposed and the corresponding travel time budget (TTB) model is derived. The budget time and reliability are compared between TTB models with and without truncated travel time distribution. Under truncated travel time distribution, the risk-averse levels of travelers are adaptive, which are affected by the characteristics of the used routes besides the confidence level of travelers. Then, a TTB-based stochastic user equilibrium (SUE) is developed to model travelers’ route choice behavior. Moreover, its equivalent variational inequality (VI) problem is formulated and a route-based algorithm is used to solve the proposed model. Numerical results indicate that route travel time boundary produces a great influence on decision cost and route choice behavior of travelers.

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Chowdhury, Saidul Azam, Bashirul Haque, and Golam Sarwar. "Traffic Information Interface Development in Route Choice Decision." Transport and Telecommunication Journal 15, no. 2 (June 1, 2014): 91–96. http://dx.doi.org/10.2478/ttj-2014-0008.

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Abstract In this paper, a method has been developed based on historic traffic data (vehicle speed), which helps the commuters to choose routes by their intelligence knowing the traffic conditions in Google maps. Data has been collected on basis of video analysis from several segments between Tuker Bazar and Bandar Bazar route. For each of the video footage, a reference length has been recorded with measurement tape for use in video analysis. Software has been also developed based on Java language to get the traffic information from historic data, which shows the output as images consisting of traffic speed details on the available routes by giving day and time limit as inputs. The developed models provide useful insights and helpful for the policy makers that can lead to the reduction of traffic congestion and increase the scope of intelligence of the road users, at least for the underdeveloped or developing country where navigation is still unavailable.

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Rowell, Maura, Andrea Gagliano, and Anne Goodchild. "Identifying truck route choice priorities: the implications for travel models." Transportation Letters 6, no. 2 (March 6, 2014): 98–106. http://dx.doi.org/10.1179/1942787514y.0000000015.

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Li, Dawei, Siqi Feng, Yuchen Song, Xinjun Lai, and Shlomo Bekhor. "Asymmetric closed-form route choice models: Formulations and comparative applications." Transportation Research Part A: Policy and Practice 171 (May 2023): 103627. http://dx.doi.org/10.1016/j.tra.2023.103627.

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Mai, Tien, The Viet Bui, Quoc Phong Nguyen, and Tho V. Le. "Estimation of recursive route choice models with incomplete trip observations." Transportation Research Part B: Methodological 173 (July 2023): 313–31. http://dx.doi.org/10.1016/j.trb.2023.05.004.

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Fujiwara, Akimasa, and Junyi Zhang. "Development of a Scheduling Model for Car Tourists’ 1-Day Tours." Transportation Research Record: Journal of the Transportation Research Board 1921, no. 1 (January 2005): 100–111. http://dx.doi.org/10.1177/0361198105192100112.

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Focusing on car tourists’ 1-day tours, a new scheduling model combines a nested paired combinatorial logit (NPCL) type of destination and route choice model and a time allocation (TA) model. The NPCL model, developed previously from the generalized extreme value family of discrete choice models to represent the similarity between pairs of alternatives in the same choice nest as well as the influence of inclusive value, indicates destination choice in the bottom level and route choice in the top level. The TA model applies Becker's theory to determine the time allocated to each touring site. Utility of destination choice is influenced by the time spent at each site. Different route choices result in a level of service for the road network that varies hourly, varying available time used in the TA model. The TA model endogenously incorporates the influence of hourly variance in level of service at the site of interest, which is affected by the allocated time. An iteration estimation procedure is proposed to estimate the parameters consistently in both models. Finally, revealed preference tourist travel survey data collected in a tourist attraction region near the Sea of Japan indicate that the proposed scheduling model is effective in representing car tourists’ scheduling behavior for 1-day tours.

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Bekhor, Shlomo, Tomer Toledo, and Joseph N. Prashker. "EFFECTS OF CHOICE SET SIZE AND ROUTE CHOICE MODELS ON PATH-BASED TRAFFIC ASSIGNMENT." Transportmetrica 4, no. 2 (January 2008): 117–33. http://dx.doi.org/10.1080/18128600808685682.

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Vitetta, Antonino. "The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem." Algorithms 16, no. 1 (January 10, 2023): 47. http://dx.doi.org/10.3390/a16010047.

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Given two pick-up and delivery points, the best path chosen does not necessarily follow the criteria of minimum travel time or generalized minimum cost evaluated with a deterministic approach. Given a criterion, the perceived cost is not deterministic for many reasons (congestion, incomplete information on the state of the system, inexact prediction of the system state, etc.). The same consideration applies to the best-chosen route, assuming that the route is an ordered list of network nodes to visit. The paths and routes perceived and chosen (drivers or companies) could follow different criteria (i.e., minizmum congested travel time for the path and minimum monetary cost for the route). In this context, the paths chosen between two pick-up and delivery points, studied with the path choice problem (PCP), influence the best route, studied with the vehicle routing problem (VRP). This paper reports some considerations on the importance of modelling the path choice behavior in the VRP; the influence of the PCP on the VRP is studied. The considerations are supported by a numerical example in a small network in which the results obtained by adopting the deterministic or probabilistic models for the PCP are compared. To validate the reported thesis, the models are applied in a small test system, and it allows the reader to follow the numerical results step by step.

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Ton, Danique, Oded Cats, Dorine Duives, and Serge Hoogendoorn. "How Do People Cycle in Amsterdam, Netherlands?: Estimating Cyclists’ Route Choice Determinants with GPS Data from an Urban Area." Transportation Research Record: Journal of the Transportation Research Board 2662, no. 1 (January 2017): 75–82. http://dx.doi.org/10.3141/2662-09.

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Nowadays, the bicycle is seen as a sustainable and healthy substitute for the car in urban environments. The Netherlands is the leading country in bicycle use, especially in urban environments. Yet route choice models featuring inner-city travel that includes cyclists are lacking. This study estimated a cyclists’ route choice model for the inner city of Amsterdam, Netherlands, on the basis of 3,045 trips collected with GPS data. The main contribution of this study was the construction of the choice set with an empirical approach, which used only the observed trips in the data set to compose the choice alternatives. The findings suggested that cyclists were insensitive to separate cycle paths in Amsterdam, a city characterized by a dense cycle path network in which cycling was the most prominent mode of travel. In addition, cyclists were found to minimize travel distance and the number of intersections per kilometer. The impact of distance on route choice increased during the morning peak when schedule constraints were more prevalent. Furthermore, overlapping routes were more likely to be chosen by cyclists, everything else being the same.

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Hensz, Christopher Michael. "Environmental factors in migratory route decisions: a case study on Greenlandic Arctic Terns (Sterna paradisaea)." Animal Migration 2, no. 1 (January 1, 2015): 76–85. http://dx.doi.org/10.1515/ami-2015-0004.

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AbstractIdentification and characterization of seasonal migration routes and stopover sites has been recognized as important to the conservation of migratory species. This project utilizes multiple regression models including circular-linear regression to identify associations between route choice, travel speed, and environmental preferences using trajectory data of migratory Arctic Terns (Sterna paradisaea) and environmental data obtained through remote-sensing techniques. Results of this study suggest that route choice on the southward post-breeding migration route may be more dependent on underlying environment than the northward postwintering migration route. In contrast, travel speed was variably associated with underlying environment between southward and northward migrations, including several differences regarding the impact of interactions between environmental variables. These results reveal the importance of using multiple metrics in the estimation of spatial resistance and highlight conflicts between the theoretical resistance framework of GIS and movement analysis methods.

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Stinson, Monique A., and Chandra R. Bhat. "Commuter Bicyclist Route Choice: Analysis Using a Stated Preference Survey." Transportation Research Record: Journal of the Transportation Research Board 1828, no. 1 (January 2003): 107–15. http://dx.doi.org/10.3141/1828-13.

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The importance of factors affecting commuter bicyclists’ route choices was evaluated. Both route-level (e.g., travel time) and link-level (e.g., pavement quality) factors are examined. Empirical models are estimated using data from a stated preference survey conducted via the Internet. The models indicate that, for commuter bicyclists, travel time is the most important factor in choosing a route. Presence of a bicycle facility (especially a bike lane or separate path), the level of automobile traffic, pavement or riding surface quality, and presence of a bicycle facility on a bridge are also very important determinants. Furthermore, there are policy implications of these results for bicycle facility planning.

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Journal articles on the topic 'Route choice models'

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