scholarly journals Modeling Stochastic Route Choice Behaviors with Equivalent Impedance

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Jun Li ◽  
Yulin Huang ◽  
Xinjun Lai
Keyword(s):  
Large Scale ◽  
Choice Model ◽  
Route Choice ◽  
Multinomial Logit Model ◽  
Closed Form Expression ◽  
Travel Costs ◽  
Scale Parameters ◽  
Equivalent Impedance ◽  
Proposed Model ◽  
Large Scale Networks

A Logit-based route choice model is proposed to address the overlapping and scaling problems in the traditional multinomial Logit model. The nonoverlapping links are defined as a subnetwork, and its equivalent impedance is explicitly calculated in order to simply network analyzing. The overlapping links are repeatedly merged into subnetworks with Logit-based equivalent travel costs. The choice set at each intersection comprises only the virtual equivalent route without overlapping. In order to capture heterogeneity in perception errors of different sizes of networks, different scale parameters are assigned to subnetworks and they are linked to the topological relationships to avoid estimation burden. The proposed model provides an alternative method to model the stochastic route choice behaviors without the overlapping and scaling problems, and it still maintains the simple and closed-form expression from the MNL model. A link-based loading algorithm based on Dial’s algorithm is proposed to obviate route enumeration and it is suitable to be applied on large-scale networks. Finally a comparison between the proposed model and other route choice models is given by numerical examples.

scholarly journals A hybrid gravity and route choice model to assess vector traffic in large-scale road networks

2020 ◽  
Vol 7 (5) ◽  
pp. 191858
Author(s):  
S. M. Fischer ◽  
M. Beck ◽  
L.-M. Herborg ◽  
M. A. Lewis
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Choice Model ◽  
Route Choice ◽  
Gravity Models ◽  
Sampling Effort ◽  
Long Distance ◽  
General Terms ◽  
Rigorous Model ◽  
Stochastic Gravity

Human traffic along roads can be a major vector for infectious diseases and invasive species. Though most road traffic is local, a small number of long-distance trips can suffice to move an invasion or disease front forward. Therefore, understanding how many agents travel over long distances and which routes they choose is key to successful management of diseases and invasions. Stochastic gravity models have been used to estimate the distribution of trips between origins and destinations of agents. However, in large-scale systems, it is hard to collect the data required to fit these models, as the number of long-distance travellers is small, and origins and destinations can have multiple access points. Therefore, gravity models often provide only relative measures of the agent flow. Furthermore, gravity models yield no insights into which roads agents use. We resolve these issues by combining a stochastic gravity model with a stochastic route choice model. Our hybrid model can be fitted to survey data collected at roads that are used by many long-distance travellers. This decreases the sampling effort, allows us to obtain absolute predictions of both vector pressure and pathways, and permits rigorous model validation. After introducing our approach in general terms, we demonstrate its benefits by applying it to the potential invasion of zebra and quagga mussels ( Dreissena spp.) to the Canadian province British Columbia. The model yields an R 2 -value of 0.73 for variance-corrected agent counts at survey locations.

scholarly journals A Multi-Modal Route Choice Model with Ridesharing and Public Transit

2018 ◽  
Vol 10 (11) ◽  
pp. 4275 ◽  
Author(s):  
Meng Li ◽  
Guowei Hua ◽  
Haijun Huang
Keyword(s):  
Public Transit ◽  
Choice Model ◽  
Route Choice ◽  
Smart Phone ◽  
Travel Cost ◽  
Payment Systems ◽  
Key Factors ◽  
Main Road ◽  
Proposed Model ◽  
Modal Route

With the extensive use of smart-phone applications and online payment systems, more travelers choose to participate in ridesharing activities. In this paper, a multi-modal route choice model is proposed by incorporating ridesharing and public transit in a single-origin-destination (OD)-pair network. Due to the presence of ridesharing, travelers not only choose routes (including main road and side road), but also decide travel modes (including solo driver, ridesharing driver, ridesharing passenger, and transit passenger) to minimize travelers’ generalized travel cost (not their actual travel cost due to the existence of car capacity constraints). The proposed model is expressed as an equivalent complementarity problem. Finally, the impacts of key factors on ridesharing behavior in numerical examples are discussed. The equilibrium results show that passengers’ rewards and toll charge of solo drivers on main road significantly affect the travelers’ route and mode choice behavior, and an increase of passengers’ rewards (toll) motivates (forces) more travelers to take environmentally friendly travel modes.

Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing

2000 ◽  
Vol 1725 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Toshiyuki Yamamoto ◽  
Satoshi Fujii ◽  
Ryuichi Kitamura ◽  
Hiroshi Yoshida
Keyword(s):  
Time Allocation ◽  
Choice Model ◽  
Stated Preference ◽  
Congestion Pricing ◽  
Route Choice ◽  
Multinomial Logit Model ◽  
Allocation Model ◽  
Departure Time ◽  
Departure Time Choice ◽  
Pricing Schemes

Driver behavior under congestion pricing is analyzed to evaluate the effects of alternative congestion pricing schemes. The analysis, which is based on stated preference survey results, focuses on time allocation, departure time choice, and route choice when a congestion pricing scheme is implemented on toll roads in Japan. A unique feature of the model system of this study is that departure time choice and route choice are analyzed in conjunction with the activities before and after the trip. A time allocation model is developed to describe departure time choice, and a route and departure time choice model is developed as a multinomial logit model with alternatives representing the choice between freeways and surface streets and, for departure time, the choice from among before, during, or after the period when congestion pricing is in effect. The results of the empirical analysis suggest that departing during the congestion pricing period tends to have higher utilities and that a worker and a nonworker have quite different utility functions. The comparative analysis of different congestion pricing schemes is carried out based on the estimated parameters. The results suggest that the probability of choosing each alternative is stable even if the length of the congestion pricing period changes, but a higher congestion price causes more drivers to change the departure time to before the congestion pricing period.

Dynamic Route Choice Model of Large-Scale Traffic Network

1997 ◽  
Vol 123 (4) ◽  
pp. 276-282 ◽  
Author(s):  
David E. Boyce ◽  
Der-Horng Lee ◽  
Bruce N. Janson ◽  
Stanislaw Berka
Keyword(s):  
Large Scale ◽  
Choice Model ◽  
Route Choice ◽  
Traffic Network ◽  
Dynamic Route

scholarly journals A bounded path size route choice model excluding unrealistic routes: Formulation and estimation from a large-scale GPS study

2021 ◽  
pp. 1-59
Author(s):  
Lawrence Christopher Duncan ◽  
David Paul Watling ◽  
Richard Dominic Connors ◽  
Thomas Kjær Rasmussen ◽  
Otto Anker Nielsen
Keyword(s):  
Large Scale ◽  
Choice Model ◽  
Route Choice

Estimating Path Travel Costs for Heterogeneous Users on Large-Scale Networks: Heuristic Approach to Integrated Activity-Based Model–Dynamic Traffic Assignment Models

2017 ◽  
Vol 2667 (1) ◽  
pp. 119-130 ◽  
Author(s):  
Xiang (Alex) Xu ◽  
Fatemeh Fakhrmoosavi ◽  
Ali Zockaie ◽  
Hani S. Mahmassani
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Heuristic Approach ◽  
Dynamic Traffic ◽  
Travel Costs ◽  
Vehicle Trajectories ◽  
Optimal Paths ◽  
Large Scale Networks ◽  
Generalized Cost

Integrating activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) have gained attention from transportation planning agencies seeking tools to address the arising planning challenges as well as transportation policies such as road pricing. Optimal paths with least generalized cost are needed to route travelers at the DTA level, while at the ABM level, only the least generalized cost information is needed (without fully specified paths). Thus, rerunning (executing) the least generalized cost path-finding algorithm at each iteration of ABM and DTA does not seem to be efficient, especially for large-scale networks. Furthermore, storing the dynamic travel cost skims for multiclass users as an alternative approach is not efficient either in regard to memory requirements. In this study, the aim was to estimate the least generalized cost so as to be used in destination and mode choice models at the ABM level. A heuristic approach was developed to use the simulated vehicle trajectories that were assigned to the optimal paths in the DTA level to estimate different cost measures, including distance, time, and monetary cost associated with the least generalized cost path for any given combination of the origin, destination, and departure time (ODT) and value of time. The proposed approximation method presented in this study used vehicle trajectories, aligned with the origin–destination direction and located in a specific boundary shaping an ellipse around the origin and destination zones at a certain time window, to estimate travel costs for the given ODT and user class. Numerical results for two real-world networks suggest the applicability of the method in large-scale networks in addition to its lower computational burden, including solution time and memory requirements, relative to other alternative approaches.

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