Modeling Driver Psychological Deliberation During Dynamic Route Selection Processes

Talaat, Hoda; Abdulhai, Baher

doi:10.1109/itsc.2006.1706823

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…Due to the lack of the observation of how probably each route is chosen for an OD pair with multiple routes, most of the past studies focus on building route choice models from empirical perspective. They assume that all passengers have full knowledge of the transportation when attempting to minimize some objective functions e.g., minimizing their travel time (user equilibrium) or minimizing the total system travel time (system optimum) [4], [5], [6]. However, those models depend heavily on behavior assumptions and lack in reliable supporting data.…”

Section: Related Workmentioning

confidence: 99%

“…A general trip of a passenger in metro system can be depicted as 5 steps as shown in Figure 4: (1) passing through entrance gate and walking to the platform, (2) waiting on the platform for a train, (3) boarding a train and staying on the train until the train reaches the passenger's destination, (4) getting off the train and exiting the metro system. To be noted, if the passenger needs to transfer, before step(4), (5) transit between platforms needs to be considered. So the whole trip duration is composed of: entry time (ETT), wait time (WTT), on train (OTT), transfer time (TFT), and exit time (EXT).…”

Section: B Extracting All Possible Plans Chosen By Each Passengermentioning

confidence: 99%

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Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

Zhao

Zhang

et al. 2017

IEEE Trans. Intell. Transport. Syst.

105

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Nowadays, metro systems play an important role in meeting the urban transportation demand in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of Automated Fare Collection(AFC) systems opens up a new opportunity. However, only each trip's tap-in and tap-out timestamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, which are necessary to solve our problem. While existing methods work well in some specific situations, they don't work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. We develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. We validate our approach using a large scale data set collected from the Shenzhen metro system. The measured results provide us with useful inputs when building the passenger path choice model.

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Section: Related Workmentioning

confidence: 99%

Section: B Extracting All Possible Plans Chosen By Each Passengermentioning

confidence: 99%

Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

Zhao

Zhang

et al. 2017

IEEE Trans. Intell. Transport. Syst.

105

View full text Add to dashboard Cite

show abstract

“…In recent years, although a number of researches have been undertaken in various personalized route planning and location-based services for improving way-finding services (Richter, 2009;Akasaka and Onisawa, 2008;Volkel and Weber, 2008;Park et al, 2007;Letchner et al, 2006;Zipf and Jost, 2006;Talaat and Abdulhai, 2006;Klippel and Winter, 2005;Rinner and Raubal, 2004;Winter, 2002), the previous researches designed in personalized multi-criteria route planning, which consider a driver's opinions in route selection (Sadeghi Niaraki and Kim, 2009;Nadi and Delavar, 2011) are still based on reducing these types of problems to a single-criterion shortest/optimum path problem (SSPP) by using a weighted linearcombination of all criteria for each edge of the network as an objective function. The studies done by Mooney and Winstanley (2006), Corne et al (2003), and Pereira (2004) indicate this type of reduction is a radical simplification of a complex problem, and in reality the MSPP does not respond to this reduction satisfactorily.…”

Section: Introductionmentioning

confidence: 99%

Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem

Pahlavani

Delavar

Frank

2012

International Journal of Applied Earth Observation and Geoinfor

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“…As a result of the move toward developing more realistic driver-oriented models, the second group of models has been recently gaining significant momentum. Examples of these models include random utility models (4,5), random regret-minimization models (6), probabilistic models (7), cognitive psychology-based models (8,9), fuzzy models (10), and models based on data mining, sometimes referred to as user models (11)(12)(13)(14).…”

mentioning

confidence: 99%

Network Route-Choice Evolution in a Real-World Experiment

Tawfik

Rakha

2012

Transportation Research Record: Journal of the Transportation R

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Route choice models are a cornerstone in many transportation engineering applications. Two main types of route choice models can be found in the literature: first, mathematical network-oriented models such as stochastic user equilibrium and, second, behavioral driver-oriented models such as random utility models. Although the former models are much more widely used in the transportation engineering realm, evidence of their inadequacy is growing continuously. The degree of their inadequacy, however, remains debatable. Two major criticisms of the theory are the unrealistic assumptions of human perceptions and the inability to incorporate driver heterogeneity. However, attempts to incorporate driver heterogeneity into the behavioral driver-oriented route choice models, too, are still inadequate. Another major limitation in the literature is that because of cost and past technological limitations, only a few studies are based on real-world experiments. Most studies are based on either stated preference surveys or travel simulators. This work analyzes results of a real-world route choice experiment with a sample of 20 drivers who made more than 2,000 real-world choices. Network and driver learning evolutions were recorded and analyzed. Findings of the experiment include the following: (a) with learning and network experience, real-world route choice percentages seem to be converging to specific values; however, these values are mostly different from those derived by using stochastic user equilibrium expectations; (b) four types of heterogeneous driver learning and choice evolution patterns are identified; and (c) the identified learning patterns are modeled and found predictable on the basis of driver and choice situation variables.

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Modeling Driver Psychological Deliberation During Dynamic Route Selection Processes

Cited by 13 publications

References 6 publications

Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem

Network Route-Choice Evolution in a Real-World Experiment

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