Bin Ran

Origin-Destination Estimation for Non-Commuting Trips Using Location-Based Social Networking Data

The development of sustainable mobility solutions calls for significant advances in travel demand data collection beyond the long-term static planning data usually available at planning agencies. This paper proposes a combined clustering, regression, and gravity model to estimate an origin-destination (OD) matrix for non-commuting trips based on Foursquare user check-in data in the Chicago urban area. The estimated OD matrix is found to be similar to the ground-truth OD matrix obtained from CMAP (Chicago Metropolitan Agency for Planning). The potential applications for generating day-of-the-week and dynamic bihourly OD patterns from Foursquare data are also illustrated.

A Novel Abandoned Object Detection System Based on Three-Dimensional Image Information

A new idea of an abandoned object detection system for road traffic surveillance systems based on three-dimensional image information is proposed in this paper to prevent traffic accidents. A novel Binocular Information Reconstruction and Recognition (BIRR) algorithm is presented to implement the new idea. As initial detection, suspected abandoned objects are detected by the proposed static foreground region segmentation algorithm based on surveillance video from a monocular camera. After detection of suspected abandoned objects, three-dimensional (3D) information of the suspected abandoned object is reconstructed by the proposed theory about 3D object information reconstruction with images from a binocular camera. To determine whether the detected object is hazardous to normal road traffic, road plane equation and height of suspected-abandoned object are calculated based on the three-dimensional information. Experimental results show that this system implements fast detection of abandoned objects and this abandoned object system can be used for road traffic monitoring and public area surveillance.

Reducing the Error Accumulation in Car-Following Models Calibrated With Vehicle Trajectory Data

With the development of probe vehicle technologies and the emerging connected vehicle technologies, applications and models using trajectory data for calibration and validation significantly increase. However, the error accumulation issue accompanied by the calibration process has not been fully investigated and addressed. This paper explores the mechanism and countermeasures of the error accumulation problems of car-following models calibrated with microscopic vehicle trajectory data. In this paper, we first derive the error dynamic model based on an acceleration-based generic car-following model formulation. The stability conditions for the error dynamic model are found to be different from the model stability conditions. Therefore, adjusting feasible ranges of model parameters in the car-following model calibration to ensure model stability cannot guarantee the error stability. However, directly enforcing those error stability conditions can be ineffective, particularly when explicit formulations are difficult to obtain. To overcome this issue, we propose several countermeasures that incorporate error accumulation indicators into the error measures used in the calibration. Numerical experiments are conducted to compare the traditional and the proposed error measures through the calibration of five representative car-following models, i.e., General Motors, Bando, Gipps, FREeway SIMulation (FRESIM), and intelligent driver model (IDM) models, using field trajectory data. The results indicate that the weighted location mean absolute error (MAE) and the location MAE with crash rate penalty can achieve the best overall error accumulation performance for all five models. Meanwhile, traditional error measures, velocity MAE, and velocity Theils U also achieve satisfactory error accumulation performance for FRESIM and IDM models, respectively.

Decentralized Cooperative Lane-Changing Decision-Making for Connected Autonomous Vehicles*

In this paper, we proposed a decentralized cooperative lane-changing decision-making framework for connected autonomous vehicles, which is composed of three modules, i.e., state prediction, candidate decision generation, and coordination. In other words, each connected autonomous vehicle makes cooperative lane-changing decision independently. In the state prediction module, we employed existing cooperative car-following models to predict the vehicles’ future state. In the candidate decision generation module, we proposed incentive based model to generate a candidate decision. In the candidate decision coordination module, we proposed an algorithm to avoid candidate lane-changing decision that may lead to a vehicle collision or traffic deterioration to be final decision. Moreover, the effects of decentralized cooperative lane-changing decision-making framework on traffic stability, efficiency, homogeneity, and safety are investigated in a numerical simulation experiment. Some stability, efficiency, homogeneity, and safety indicators are evaluated and show the high potential of our proposed framework in traffic dynamics.

Restoration of Motion-Blurred Image Based on Border Deformation Detection: A Traffic Sign Restoration Model

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.

Freeway recurrent bottleneck identification algorithms considering detector data quality issues

Computer algorithms used to identify recurrent freeway bottlenecks have been studied since the deployment of loop detecting systems. Such algorithms automatically analyze the archived loop detector data and identify potential recurrent bottlenecks and their characteristics, such as location, time of day, and activation rate, for further investigation. In a highway congestion mitigation project, such algorithms can save time and resources for the initial screening of bottlenecks over a large freeway network. These algorithms include rule-based, contour-map-based, and simulation-based methods. However, existing methods require loop detector data with high accuracy and consistency, which is difficult to achieve in prevailing loop detecting systems. This paper proposes a new bottleneck identification algorithm with strong error and noise tolerance. Several simple denoising methods to improve the error resistance of existing algorithms are also proposed. Using statistical error analysis methods, the proposed algorithm and the denoising methods were calibrated and evaluated using field data collected from two distinct freeway corridors (US 12/14 and I-894) in the U.S. state of Wisconsin. Ground truth data for this study come from the manual inspection of 287,055 traffic video snapshots in the course of a month. In the evaluation tests, the proposed algorithm can produce quality congestion identification results with fewer false alarms than the existing algorithms, especially when identifying severe bottleneck congestion.

Characterizing Passenger Flow for a Transportation Hub Based on Mobile Phone Data

As the vital node of a passenger transportation network, the transportation hub is the connection between multiple travel modes and the important port for the massive passenger flow to enter into or exit from a city area. Transportation operators need to understand the passenger flow pattern for hub management, transportation planning, and so on. However, it is difficult to use traditional methods, such as video detection, to provide such information. With the increasing number of mobile phone users, mobile phone data have shown remarkable potential in detecting the transportation information with high sampling coverage and low cost. This paper utilizes the mobile phone data to characterize the passenger flow of the Hongqiao transportation hub located in Shanghai, China. First, a temporal-spatial clustering method is proposed to identify the passenger active area of the Hongqiao hub in the wireless communication space. Second, a classification process is presented to extract different types of passengers in this transportation hub. Subsequently, the access characteristics of passengers in the city are studied for various time intervals. The results further verify the potential of using mobile phone data to monitor and characterize passenger flow related to the transportation hubs.

Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state), suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods.

Large Scale Intelligent Transportation System Traffic Detector Data Archiving

Archived traffic data can be used in transportation planning, administration, and research by various entities and agencies. During the past two decades, considerable effort has been dedicated to developing and implementing large-scale traffic data archives. The Wisconsin Traffic Operation and Safety (TOPS) Laboratory at the University of Wisconsin–Madison maintains a statewide traffic detector data archiving and retrieving system, which is developed to enable centralized management of statewide ITS detector and configuration data, optimizing the utilization of massive data on a systematic level, and improving the interactivity and accessibility for integration with other transportation data sources such as lane closure data or incident data. This data archive is currently being enhanced to incorporate higher resolution traffic data by migrating from 5-minute to 1-minute and even 20-second sampling intervals. At the same time, there is a desire to generate aggregated datasets such as hourly, monthly, and annual average values from the raw data. As the traffic data requirements continue to grow, the management of the traffic data archive becomes a complex big data problem. This paper describes a proposed redesign of the TOPS Lab traffic detector archived data management system to improve storage, performance, access, and integration capabilities. Particular detail is given to the data archiving process, including data validation, and support for spatial attributes and GIS data integration.

Same-Day Mode Choice Modeling with Household Vehicle Usage Simulation in Developing Countries

This paper presents a model of same-day mode choice at the household level for developing countries. A rule-based algorithm combining classical random utility maximization theory within a microsimulation framework is used. Modeling of private vehicle usage (including vehicle allocation and sharing use in household) is an essential component of this model because vehicle deficiency is common in developing countries. This model consists of four steps: (a) the allocation of private vehicles (car, motorcycle, and bicycle) in a household, (b) the mode choice of private vehicle users specified in the first step, (c) vehicle sharing in a household, and (d) the mode choice of individuals who do not use private vehicles. The adaptability of the model was improved by simulations on car, motorcycle, and bicycle usage. Discrepancies in the mode choice behavior of household members with and without the use of private vehicles are captured in this paper through different modeling methods. The rule-based algorithm, binary logit model, multinomial logit model, and mixed logit model were applied together in this four-step model. Travel diary survey data from 2007 from Bengbu, China, were used as an example for the validation test of this model. The results demonstrate that this model can accurately predict the mode choice of all household members in an internally self-consistent and theoretically credible manner for a midsize city in China. The proposed model is highly conducive to travel demand forecasting and transportation policy making.