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Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.

The problem of estimating origin-destination travel demands from partial observations of traffic conditions has often been formulated as a network design problem (NDP) with a bi-level structure. The upper level problem in such a formulation minimizes a distance metric between measured and estimated traffic conditions, and the lower level enforces user-equilibrium traffic conditions in the network. Since bi-level problems are usually challenging to solve numerically, especially for large-scale networks, we proposed, in an earlier effort (Nie et al., Transp Res , 39B:497–518, 2005), a decoupling scheme that transforms the O–D estimation problem into a single-level optimization problem. In this paper, a novel formulation is proposed to relax the user equilibrium conditions while taking users’ route choice behavior into account. This relaxation approach allows the development of efficient solution procedures that can handle large-scale problems, and makes the integration of other inputs, such as path travel times and historical O–Ds rather straightforward. An algorithm based on column generation is devised to solve the relaxed formulation and its convergence is proved. Using a benchmark example, we compare the estimation results obtained from bi-level, decoupled and relaxed formulations, and conduct various sensitivity analysis. A large example is also provided to illustrate the efficiency of the relaxation method.

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.

D-estimation method (DEM) is a single-phase synchronization system, which includes a prefilter, a postfilter, an estimate producer, and a frequency feedback (FFB) loop for adapting the prefilter and postfilter to frequency changes. The DEM has a completely different structure compared to phase-locked loops (PLLs), which makes its analysis, tuning, and linear modeling rather challenging, at least for those who are not very experienced in this area. This study aims to bridge this research gap. It is demonstrated in this study that the DEM, in its simplest possible form, is mathematically equivalent to the second-order generalized integrator-based PLL (SOGI-PLL) if a certain condition holds. This equivalence, which is verified numerically and experimentally, makes the linear modeling, analysis, and tuning of the DEM quite straightforward.

As more people choose to travel by bicycle, transportation planners are beginning to recognize the need to rethink the way they evaluate and plan transportation facilities to meet local mobility needs. A modal shift towards bicycles motivates a change in transportation planning to accommodate more bicycles. However, the current methods to estimate bicycle volumes on a transportation network are limited. The purpose of this research is to address those limitations through the development of a two-stage bicycle origin–destination (O–D) matrix estimation process that would provide a different perspective on bicycle modeling. From the first stage, a primary O–D matrix is produced by a gravity model, and the second stage refines that primary matrix generated in the first stage using a Path Flow Estimator (PFE) to build the finalized O–D demand. After a detailed description of the methodology, the paper demonstrates the capability of the proposed model for a bicycle demand matrix estimation tool with a real network case study.

While traffic speed data and travel time estimates are increasingly more available from commercial vendors, they are not sufficient for proper management and performance evaluation of transportation networks. Effective traffic control and demand management requires information about volumes, which is provided by fixed location sensors, such as loop detectors or cameras, and those are sparse. This paper proposes a method for estimating route choice using sparse flow measurements and estimated speed on the road network based on compressed sensing technology widely used in image processing, where from a handful of scattered pixels, a full image is recovered. What is known includes flows at origins and at selected links of the road network, where the detection is present; speed estimates are available for all network links. We find coefficients that split origin flows among routes starting at those origins. The advantage of the proposed methodology is that it does not rely on simulation that is prone to calibration errors but only on measured data. We also show how vehicle flows can be estimated at links with no detection, which enables computing performance measures for road networks lacking complete sensor coverage. Finally, we propose a method for selecting plausible routes between origins and destinations.

Merge junctions are the key elements in the freeway system, as they are likely to function as bottlenecks. Investigations into breakdown occurrence at ramp junctions have demonstrated that when the groups of several vehicles following each other enter the freeway from the ramp, they are expected to create 'turbulence' resulting from lane changes, decelerations of vehicles on the mainline and inevitably by the cars merging from the on-ramp. This turbulence can lead to breakdown when the level of mainline demand is adequately high. In other words, the impact of a ramp vehicle on capacity is higher than that of a mainline vehicle, which indicates that a part of vehicles will simultaneously occupy two lanes during the process of changing them thus momentarily decreasing the capacity of the link. This feature becomes particularly important near bottlenecks where it might reduce the already limited throughput. The article introduces the main approaches, methodology, principles and stages of transport demand management on freeways that are aimed at improving the operation quality of transport facilities, including road safety. The technique allows evaluating and optimizing a Ramp-Metering (RM) concept from the viewpoint of minimizing the length of queues on ramps and a possibility of transport congestion. The proposed algorithm estimates the probability of starting congestion formation on the ramp using objective information on traffic conditions in each segment of the highway, which is based on the criterion for vehicle density on the lane. The last chapter shows the examples of traffic flow optimization on Western bypass ramps in Vilnius comparing two strategies for access control management using one or several vehicles per lane. Conclusions, trends and work on future investigations are presented at the end of the article.

Public transportation system as an essential mode of travel has been investigated by local governments and transportation agencies to capture passengers’ travel behaviors. Despite their efforts, agencies especially in small to medium sized cities could not afford to collect such behaviors data due to significant costs associated with the data collection system. In this study, we presented a WiFi sensing system which makes such data collection feasible with low-cost devices. We demonstrated the WiFi sensing system’s applicability in estimating passengers’ origin-destination (O/D) travel and passengers’ bus stop waiting times via video validation. In addition, WiFi signal strength was analyzed to further improve accuracy of the system. To this end, sliding window algorithm was adopted to mitigate the randomness of mobile devices’ signals. Our small-scale proof of concept experiment was conducted at four bus stops along the main transit corridor in Charlottesville, Virginia. Results indicated that the system was able to re-identify 91% of bus passengers and passengers bus stop waiting time error was as small as 7 seconds. It is expected that the system can be a viable low-cost Internet of Things (IoT) solution for monitoring public transit system performance.

Most existing network sensor location problem (NSLP) models are designed to identify the number of sensors with fixed costs and installation locations, and sensors are assumed to be installed permanently. However, sometimes sensors are carried by individuals to collect traffic data measurements manually at fixed locations. Hence, their duration of operation for which traffic data measurements are collected is limited, and their costs are not fixed as they are correlated with the duration of operation. This paper proposes a NSLP model that integrates optimal heterogeneous sensor deployment and operation strategies for the dynamic O-D demand estimates under budget constraints. The deployment strategy consists of the numbers of link and node sensors and their installation locations. The operation strategy includes sensors’ start time and duration of operation, which has not been addressed in previous studies. An algorithm is developed to solve the proposed model. Numerical experiments performed on a network from a part of Chennai, India show that the proposed model can identify the optimal heterogeneous sensor deployment and operation strategies with the maximum dynamic O-D demand estimation accuracy.