Alireza Khani

Intermodal Path Algorithm for Time-Dependent Auto Network and Scheduled Transit Service

A simple but efficient algorithm is proposed for finding the optimal path in an intermodal urban transportation network. The network is a general transportation network with multiple modes (auto, bus, rail, walk, etc.) divided into the two major categories of private and public, with proper transfer constraints. The goal was to find the optimal path according to the generalized cost, including private-side travel cost, public-side travel cost, and transfer cost. A detailed network model of transfers between modes was used to improve the accounting of travel times during these transfers. The intermodal path algorithm was a sequential application of specific cases of transit and auto shortest paths and resulted in the optimal intermodal path, with the optimal park-and-ride location for transferring from private to public modes. The computational complexity of the algorithm was shown to be a significant improvement over existing algorithms. The algorithm was applied to a real network within a dynamic traffic and transit assignment procedure and integrated with a sequential activity choice model.

Algorithm for Intermodal Optimal Multidestination Tour with Dynamic Travel Times

This paper presents an efficient algorithm that finds the intermodal optimal tour (origin to origin) in a time-dependent transportation network while the algorithm implicitly solves the park-and-ride facility choice problem with the inherent park-and-ride constraints for a traveler with a sequence of destinations to visit. To solve the problem, a network expansion technique that captures the constraints of park-and-ride behavior in the model and that transforms the park-and-ride choice problem into a dynamic network flow problem is introduced. An efficient iterative labeling algorithm that finds the optimal intermodal tour to serve the sequence of activities is also introduced. Multisource shortest-path runs are used in the iterative labeling algorithm to find the optimal tour with several intermediate destinations in an efficient manner. The performance of the algorithm is compared with the performance of existing approaches, and improvement is indicated. The solution method proposed benefits from the advantages of Dijkstras shortest-path algorithm, which is made possible by (a) a nontrivial transformation of the original problem into a dynamic network flow problem and (b) an innovative use of a multisource shortest path in the context of origin–destination choice. The solution algorithm integrates time-dependent auto and transit shortest-path algorithms to find the optimal tour. The algorithm is implemented, coded, and tested on a real network, and the results are promising.

Transfer optimization in transit networks: Headway and departure time coordination

This paper studies the scheduling problem in transit networks in order to decrease transfer waiting time. Transfer waiting time is calculated based on headway and departure time of intersecting routes and is divided into two parts. The first part can be reduced by changing departure times and was studied by the authors previously. The focus of the present research, however, is to minimize the second part of the transfer waiting time, dependent on the headways. The proposed optimization model in this paper includes both parts and is a nonlinear mathematical programming model. The model is decomposed to the departure time setting model (DSM) and the headway setting model (HSM). A solution method based on a genetic algorithm is also suggested to solve the model for large transit networks. Results of a case study show good performance of the model and the solution algorithm.

Hyperpaths in Network Based on Transit Schedules

The concept of a hyperpath was introduced for handling passenger strategies in route choice behavior for public transit, especially in a frequency-based transit service environment. This model for handling route choice behavior has been widely used for planning transit services, and hyperpaths are now applied in areas beyond public transit. A hyperpath representing more specific passenger behaviors on a network based on transit schedules is proposed. A link-based time-expanded (LBTE) network for transit schedules is introduced; in the network each link represents a scheduled vehicle trip (or trip segment) with departure time and travel time (or arrival time) between two consecutive stops. The proposed LBTE network reduces the effort to build a network based on transit schedules because the network is expanded with scheduled links. A link-based representation of a hypergraph with existing hyperpath model properties that is directly integrated with the LBTE network is also proposed. Transit passenger behavior was incorporated for transfers in the link-based hyperpath. The efficiency of the proposed hyperpath model was demonstrated. The proposed models were applied on a test network and a real transit network represented by the general specification of Googles transit feed.

Map-Matching Algorithm for Applications in Multimodal Transportation Network Modeling

Generalized Transit Feed Specification (GTFS) files have gained wide acceptance by transit agencies, which now provide them for most major metropolitan areas. The public availability GTFSs combined with the convenience of presenting a standard data representation has promoted the development of numerous applications for their use. Whereas most of these tools are focused on the analysis and utilization of public transportation systems, GTFS data sets are also extremely relevant for the development of multimodal planning models. The use of GTFS data for integrated modeling requires creating a graph of the public transportation network that is consistent with the roadway network. The former is not trivial, given limitations of networks often used for regional planning models and the complexity of the roadway system. A proposed open-source algorithm matches GTFS geographic information to existing planning networks and is also relevant for real-time in-field applications. The methodology is based on maintaining a set of candidate paths connecting successive geographic points. Examples of implementations using traditional planning networks and a network built from crowdsourced OpenStreetMap data are presented. The versatility of the methodology is also demonstrated by using it for matching GPS points from a navigation system. Experimental results suggest that this approach is highly successful even when the underlying roadway network is not complete. The proposed methodology is a promising step toward using novel and inexpensive data sources to facilitate and eventually transform the way that transportation models are built and validated.

Modeling Transit and Intermodal Tours in a Dynamic Multimodal Network

A fixed-point formulation and a simulation-based solution method were developed for modeling intermodal passenger tours in a dynamic transportation network. The model proposed in this paper is a combined model of a dynamic traffic assignment, a schedule-based transit assignment, and a park-and-ride choice model, which assigns intermodal demand (i.e., passengers with drive-to-transit mode) to the optimal park-and-ride station. The proposed model accounts for all segments of passenger tours in the passengers’ daily travel, incorporates the constraint on returning to the same park-and-ride location in a tour, and models individual passengers at a disaggregate level. The model has been applied in an integrated travel demand model in Sacramento, California, and feedback to the activity-based demand model is provided through separate time-dependent skim tables for auto, transit, and intermodal trips.

Travel Behavior Classification: An Approach with Social Network and Deep Learning:

Uncovering human travel behavior is crucial for not only travel demand analysis but also ride-sharing opportunities. To group similar travelers, this paper develops a deep-learning-based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the years 2012 and 2013. After preprocessing and exploring the raw data, an activity matrix is constructed for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, a community social network is constructed for all participants. A community detection algorithm is further implemented to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. An image of activity map is built from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.

Transit delay estimation using stop-level automated passenger count data

AbstractDespite the potential use of global positioning system (GPS) based automatic vehicle location (AVL) data in the development of reliability improvement strategies for transit systems, issues...

A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.