Axiomatization of Transit Flow Estimation

This paper focuses on the estimation and compression of ride demand from origin-destination (OD) trip event data. By representing the OD event data as a three-way tensor (origin, destination, and time), we model the data as a Poisson process with an intensity tensor that can be decomposed according to a Tucker decomposition. We establish and justify a specific form of nonnegative Tucker-like tensor decomposition that represents OD demand via K latent origin spatial factors and K latent destination spatial factors. We then provide a computational and memory efficient algorithm for performing this decomposition and demonstrate its use for real-time compression and estimation of OD ride demand. Two case studies based on New York City (NYC) taxi and Washington DC (DC) taxi were implemented. Results from the case studies demonstrate the applicability of the proposed method in data compression and short-term forecast for ride demand. Furthermore, we found that the learned latent spatial factors are interpretable and localized to specific areas for both NYC and DC cases. Hence, this method can be used to understand OD trip data through latent spatial factors and be used to identify spatio-temporal patterns for OD trip and travel demand generation mechanism in general.Funding: This work was supported by the U.S. Department of Transportation [UTC/NCST] and the U.S. National Science Foundation [Grant DMS 1712996].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0101 .

Public transport network model based on layer operations

The ubiquitous massive mobile phone data generation presents new opportunities to determine the requirements of transportation, disaster management and public health care systems. Currently, data from mobile phone records can help in identifying the location of the users while they are making trips. Generally, this estimation is achieved using traditional data collection methods; however, these methods are difficult to apply in developing countries with rapidly growing cities owing to the high population and limitation in conducting a survey. Call detail records (CDRs) are used as base data because they are valuable data sources and can reduce the cost and time limitations. The aim of this study is to estimate origin-destination (OD) trips from each zone by using the CDRs. The OD trips are estimated by using the CDRs of one week taken from Myanmar Post and Telecommunication mobile operator for over 1.9 million users per day in Yangon, the economic center of Myanmar. The OD trips are estimated from CDRs based on the location of the base station in a limited time window and time frame. If the same mobile users is observed in two different the ones within the time limit, it is assumed that the mobile user is coming out from the first zone and the trips represents an originating trip. This trip would be the destination trip for zone where the mobile user enters. In this study, the originating (outgoing) and destination trips (incoming) from each township on a weekday and weekend are determined. These data are useful for infrastructure development and urban transportation planning.

A multidepot VRP is solved in the context of total urban traffic equilibrium. Under the total traffic equilibrium, the multidepot VRP is changed to GDAP (the problem of Grouping Customers + Estimating OD Traffic + Assigning traffic) and bilevel programming is used to model the problem, where the upper model determines the customers that each truck visits and adds the trucks’ trips to the initial OD (Origin/Destination) trips, and the lower model assigns the OD trips to road network. Feedback between upper model and lower model is iterated through OD trips; thus total traffic equilibrium can be simulated.

This paper presents a distributed optimal routing strategy that is based on an estimation of the OD (origin-destination) matrix. This routing strategy may be employed in packet-switched networks. In the most common form of distributed routing, every node acts autonomously, distributing its link delay information to all other network nodes and periodic or aperiodic computing its own routing table based upon the link delays information obtained from other network nodes. The optimal routing assumes that the OD matrix is known. In order to avoid the transportation of the OD matrix through the communication network, it is estimated based on the link delays, the transit flows and the prior OD matrix. The represented routing strategy involves a computationally intensive iterative procedure, and therefore within large networks, this routing strategy becomes impractical. In these networks, during normal operation of the network, without changes in the network topology, a primitive routing algorithm such as a distributed Frank-Wolfe with fixed stepsize might be used. In addition, every node estimates the OD matrix. Then in the case of changes in the network topology, the estimated OD matrix is used to compute the routing tables. A further application of this strategy is within the network performance monitoring.

Traffic assignment models are used to estimate and distribute flows in a road network so that congestion and travel time delay are minimized. However, most of these models require the costly origin-destination (OD) data. In this paper, through the ideal flow network (IFN) and the maximum entropy principle, traffic flows can be estimated even with limited road network data. The macro level OD matrix is mathematically transformed into micro level OD on each intersection, which yields richer transportation structure data such as capacity and lane width. When data available is limited to only a few links or intersections, the remaining missing data can be set by computing for the stochastic matrix from the capacity ratio. Even if the capacity ratio is not available, the connectivity of the road network structure can be used to derive the stochastic matrix. With this characteristic of the IFN, even with limited and parsimonious data that can be collected from any link or intersection using a video camera, GPS, or any ITS sensing device, link flows of an entire network can be updated dynamically. Link flow results using IFN are almost the same as actual results, which is illustrated using the Sioux Falls transportation network.

Under Intelligent Transportation Systems (ITS), real-time operations of traffic management measures depend on long-term planning results, such as the origin–destination (OD) trip distribution; however, results from current planning procedures are unable to provide fundamental data for dynamic analysis. In order to capture dynamic traffic characteristics, transportation planning models should play an important role to integrate basic data with real-time traffic management and control. In this paper, a heuristic algorithm is proposed to establish the linkage between daily OD trips and dynamic traffic assignment (DTA) procedures; thus results from transportation planning projects, in terms of daily OD trips, can be extended to estimate time-dependent OD trips. Field data from Taiwan are collected and applied in the calibration and validation processes. Dynamic Network Assignment-Simulation Model for Advanced Road Telematics (DYNASMART-P), a simulation-based DTA model, is applied to generate time-dependent flows. The results from the validation process show high agreement between actual flows from vehicle detectors (VDs) and simulated flows from DYNAMSART-P.

To validate traffic signal control algorithm’s performance, a setup of microscopic traffic simulation platform with realistic traffic demand is necessary. Traditionally, a bilevel framework of Origin-Destination (OD) calibration and trip assignment, is setup to estimate OD so that realistic traffic demand can be emulated in simulation platform. However, with this approach, we may mislead the calibration process by introducing insufficient green time allocation, as vehicles are likely to be stopped by red signals and thus vehicle throughput will never reach the real traffic demand. While this happens occasionally in unsaturated traffic condition, it is very prevalent in the saturated condition scenario. This paper introduces a trilevel problem formulation with consideration of traffic signal schedules during the OD estimation process. The first level uses an iterative algorithm (LSQR) to generate OD traffic demand with certain constraints based on real loop count data at junctions. Second level applies the traffic demand into a simulation platform to generate the trips between OD points. Dynamic User Equilibrium (DUE) will be satisfied iteratively so that the trip assignment is reasonable. Finally, the third level applies Iterative Tuning (IT) signal controller to tune signal schedules iteratively, such that sufficient green time can be allocated to allow vehicles drive through intersections. Via OD calibrations in corridor and area networks, we show that the trilevel OD estimation approach can achieve better performance as compared to the bi-level approach.

Estimation of origin-destination (OD) flows in transportation networks is a major step in transportation planning. We are interested in estimating OD flows given data on traffic link volumes over a sequence of days. We propose a dynamic hierarchical Bayesian model for the estimation of day-to-day OD flows. At the first level, we specify a dynamic Gaussian model which describes the evolution of OD flows over time. At the second level, we model the assignment of route flows given OD flows. Route choice probabilities are a function of user-predicted route costs, which depend on past user-experienced costs with a leaning parameter which controls the length of users memory. At the third level we model observed link volumes given route flows. Covariance matrices of OD flows and observed link volumes are modeled through variance functions. We develop a Metropolis-within-Gibbs algorithm to sample from the joint posterior distribution of OD flows, route flows, and parameters of the route choice model and the variance functions. Our model can be applied to both congested and uncongested networks and does not assume network equilibrium. We illustrate application of the model and sampling algorithm through numerical studies on two transportation networks from the literature. In a small test network, results indicate that OD flows and parameters may be identified given uninformative priors, while in a real-scale network OD flows and route choice parameters may be reasonably identified given prior knowledge on OD flows at the beginning of the planning horizon and their temporal variability.

Origin-destination (OD) trip matrices can provide traffic patterns that are crucial for transportation planning and traffic operational analysis. This paper applies an operational based platform for conducting capacity constrained OD estimation. It is tested on a 13-mile freeway section of state highway in California with an exclusive HOV lane, truck restricted lane, and mixed SOV/HOV/truck lane. Based on seed matrices created by TransCAD ODME, the observed traffic datasets including automatic traffic recorder (ATR) on highways, turning counts at on- or off-ramps are fed into Paramics estimator for OD estimation. A hybrid approach is taken to iteratively train the OD matrices with multiple vehicle classes. Later, the estimated OD tables are checked in the simulation model to evaluate their accuracy. The close match between the simulated flow and the observed traffic attests to the superior performance of this proposed method for OD estimation.

An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts

Origindestination (OD) trip matrices reflect demand patterns of traffic networks and play important roles in traffic engineering. Existing approaches for traffic flow forecast such as ARIMA, SVR, and neural network perform well in modeling and predicting each OD pair separately, but they cause inefficiency when dealing with multidimensional OD demand data. In this paper, to solve the problemgiven OD trip matrices for T moments, how can we predict the overall OD trip matrices at time T+1, T+2, or even T+L, we model temporal vehicle-based OD trip matrix as a four-order tensor consisting of four attributes: origin, destination, vehicle type and time. By resorting to CANDECOMP/PARAFAC (CP) tensor decomposition, we show how a prediction method can be used to forecast future traffic demand in time factor matrix. Experiments based on a real highway tolling dataset demonstrate that our method effectively reduces the time for prediction and meanwhile makes the prediction accuracy competitive with those of other methods.

Origin-destination (OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. We have constructed the benchmarks for these four problems at https://github.com/tsinghua-fib-lab/OD_benckmark. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow related research, fostering interdisciplinary perspectives and modes of thinking.

PCA-subspace method has been proposed for network-wide anomaly detection. Normal subspace contamination is still a great challenge for PCA although some methods are proposed to reduce the contamination. In this paper, we apply PCA-subspace method to six-month Origin-Destination (OD) flow data from the Abilene. The result shows that normal subspace contamination is mainly caused by anomalies from a few strongest OD flows, and seems unavoidable for subspace method. Further comparison of anomalies detected by subspace method and manually tagged anomalies from each OD flows, we find that anomalies detected by subspace method are mainly caused by anomalies from medium and a few large OD flows, and most anomalies of minor OD flows are buried in abnormal subspace and hard to be detected by PCA-subspace method. We analyze the reason for those anomalies undetected by subspace method and suggest to use normal subspace to detect anomalies caused by a few strongest OD flows, and to further divide abnormal subspace to detect more anomalies from minor OD flows. The goal of this paper is to address limitations neglected by prior works and further improve the subspace method on one hand, also call for novel detection methods for network-wide traffic on another hand.

Study on the Transit Network Evaluation Method Based on the Transit Ridership Model