Selected Publications:
Zheng Zhu, Atabak Mardan, Shanjiang Zhu, Hai Yang,
Transportation Research Part B: Methodological, Volume 143, 2021, Pages 48-64, ISSN 0191-2615,
https://doi.org/10.1016/j.trb.2020.11.005.
Abstract: Travel time reliability plays an important role in travelers’ route choice behaviors. Based on a previously developed generalized Bayesian traffic model, we propose different types of perceived knowledge (i.e., mean-variance-based type, relative gap-based type, and penalty-based type) to model travelers’ daily route choice behavior concerning travel time reliability. We theoretically demonstrate the flexibility of the generalized Bayesian model in capturing various existing UE-based travel behaviors and other non-UE-based travel behaviors (e.g., penalty-based) in stochastic transportation systems. Three major conclusions are obtained. First, the route choice dynamics induced by the Bayesian model with an infinitely long memory and mean-variance-based perceived knowledge will converge to the mean-variance UE condition. Second, the convergence of route choice dynamics to a UE condition is not affected by adding a bounded weight on the daily perceived knowledge. Thirdly, non-UE-based formulations of perceived knowledge also lead to fixed points for the mean route choice proportion. The convergences of the models with different types of perceived knowledge are verified based on numerical studies and the underlying day-to-day route choice dynamics with both recurrent and non-recurrent unreliability are examined.
Toward Bayesian-based data-driven transportation modeling: Capture shifting travel behavior through a high-order Markov hidden state model -(Under Review)
Zheng Zhu, Shanjiang Zhu, Lijun Sun, Atabak Mardan
Journal: Transportation Science
Manuscript ID: Draft
ABSTRACT: his study developed a Bayesian-based data-driven framework to model day-to-day transportation system dynamics. The route choice behavior is empirically estimated following a Dirichlet distribution and can be continuously updated with a stream of empirical data. By introducing a high-order Markov hidden state model, the proposed modeling framework can automatically detect the routine and sudden changes of travel decision-making paradigms and apply different patterns accordingly for the prediction of system dynamics. To achieve computing efficiency, the particle-based Markov chain Monte Carlo algorithm is used to update model parameters using the Bayes’ Theorem. This study demonstrated the feasibility of the proposed modeling framework through a numerical example on a small, but real network. With more and more passively collected spatial-temporal transportation data, the proposed descriptive modeling approach could become an attractive alternative to the conventional transportation models based on normative assumptions.
Atabak Mardan and Shanjiang Zhu
Sponsoring Agency Name and Address: US Department of Transportation Office of the UTC Program
Contract or Grant No.: DTRT13-G-UTC48
ABSTRACT: Conventional travel demand and other planning data sources provided very limited coverage on non-motorized modes such as biking and pedestrian. Crowdsourcing approach has the potential to collect more up-to-date data for these modes with minimal costs and at a continuous basis. However, such data is mostly self-reported and lacks a unified format and standard, which compromises the data quality. More advanced data processing, cleansing, and integration methods are needed to make such data sources useful and valuable. This study investigated a set of biking incidents data collected in the Washington D.C. metropolitan area to explore such potentials.
Zhu, Shanjiang; Mardan, Atabak; Yang, Zhou
Corporate Creators: Virginia Transportation Research Council (VTRC)
Corporate Contributors: Virginia Department of Transportation ; United States. Department of Transportation; Federal Highway Administration
Publication/ Report Number: FHWA/VTRC 20-R17
ABSTRACT: The Virginia Department of Transportation (VDOT) is committed to providing and maintaining transportation infrastructure for a transportation system of multiple modes, including bicycling and walking. A complete and well-maintained bicycle and pedestrian facility inventory is critical for that mission. Given the large number of bicycle and pedestrian facilities, it is impractical to rely exclusively on VDOT staff for all data collection, processing, and maintenance efforts. A crowdsourcing approach that leverages inputs from volunteers, student interns, or both offers an attractive alternative. In addition, VDOT needs better communication channels to reach out to facility users and collect feedback on facility conditions and needs. The objective of this project is to develop a practical and effective crowdsourcing method for engaging targeted users of VDOT bicycle and pedestrian facilities to improve the existing inventory and meet the data needs for investment prioritization. To achieve this objective, this project reviewed mainstream crowdsourcing approaches that have been applied in the field of transportation and evaluated their applicability in the context of this project. The project team also interviewed agencies of localities in Northern Virginia to understand their practices and bicycling and pedestrian advocacy groups to understand the perspective of potential users. On the basis of these findings, this project developed a hybrid framework to achieve the research objective by integrating geo-analysis, crowdsourcing approaches, and targeted public outreach efforts.