scholarly journals Adaptive Route Choice Models in Stochastic Time-Dependent Networks

2008 ◽  
Vol 2085 (1) ◽  
pp. 136-143 ◽  
Author(s):  
Song Gao ◽  
Emma Frejinger ◽  
Moshe Ben-Akiva
Keyword(s):  
Choice Model ◽  
Route Choice ◽  
Choice Models ◽  
Path Model ◽  
Time Dependent ◽  
Policy Choice ◽  
Revealed Preferences ◽  
Policy Model ◽  
Routing Policy ◽  
Stochastic Time

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.

Routing Policy Choice Set Generation in Stochastic Time-Dependent Networks

2014 ◽  
Vol 2466 (1) ◽  
pp. 76-86 ◽  
Author(s):  
Jing Ding ◽  
Song Gao ◽  
Erik Jenelius ◽  
Mahmood Rahmani ◽  
He Huang ◽  
...  
Keyword(s):  
Time Dependent ◽  
Policy Choice ◽  
Routing Policy ◽  
Choice Set Generation ◽  
Stochastic Time ◽  
Choice Set

Effect of Route Choice Models on Estimating Network Capacity Reliability

2000 ◽  
Vol 1733 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Anthony Chen ◽  
Maya Tatineni ◽  
Der-Horng Lee ◽  
Hai Yang
Keyword(s):  
Network Performance ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Network Capacity ◽  
Service Level ◽  
Choice Models ◽  
Transportation Systems ◽  
Traffic Demand ◽  
Route Choice Behavior

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.

A latent-class adaptive routing choice model in stochastic time-dependent networks

2019 ◽  
Vol 124 ◽  
pp. 1-17 ◽  
Author(s):  
Jing Ding-Mastera ◽  
Song Gao ◽  
Erik Jenelius ◽  
Mahmood Rahmani ◽  
Moshe Ben-Akiva
Keyword(s):  
Latent Class ◽  
Choice Model ◽  
Adaptive Routing ◽  
Time Dependent ◽  
Stochastic Time

Influence of Changes in Level on Passenger Route Choice in Railway Stations

2005 ◽  
Vol 1930 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Winnie Daamen ◽  
Piet H. L. Bovy ◽  
Serge P. Hoogendoorn
Keyword(s):  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Choice Models ◽  
Walking Distance ◽  
Choice Data ◽  
Factors Affecting ◽  
Simulation Tools ◽  
Route Choice Behavior ◽  
Transfer Station

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.

Comparing Route Choice Models for Managed Lane Networks with Multiple Entrances and Exits

2019 ◽  
Vol 2673 (10) ◽  
pp. 381-393 ◽  
Author(s):  
Venktesh Pandey ◽  
Stephen D. Boyles
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Route Choice ◽  
Choice Models ◽  
Percentage Error ◽  
Average Error ◽  
Travel Times ◽  
Expected Cost ◽  
Binary Logit Model ◽  
Binary Logit

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.

scholarly journals Understanding individual routing behaviour

2016 ◽  
Vol 13 (116) ◽  
pp. 20160021 ◽  
Author(s):  
Antonio Lima ◽  
Rade Stanojevic ◽  
Dina Papagiannaki ◽  
Pablo Rodriguez ◽  
Marta C. González
Keyword(s):  
Choice Model ◽  
Cost Minimization ◽  
Human Mobility ◽  
Route Choice ◽  
Empirical Support ◽  
Opportunity To Learn ◽  
Choice Models ◽  
Transportation Systems ◽  
Smart Devices ◽  
Link Type

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.

Sign in / Sign up

Export Citation Format

Share Document