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Vehicle model recognition (VMR) benefits the parking, surveillance, and tolling system by automatically identifying the exact make and model of the passing vehicles. Edge computing technology enables the roadside facilities and mobile cameras to achcieve VMR in real-time. Current work generally relies on a specific view of the vehicle or requires huge calculation capability to deploy the end-to-end deep learning network. This paper proposes a lightweight two-stage identification method based on object detection and image retrieval techniques, which empowers us the ability of recognizing the vehicle model from an arbitrary view. The first-stage model estimates the vehicle posture using object detection and similarity matching, which is cost-efficient and suitable to be programmed in the edge computing devices; the second-stage model retrieves the vehicle’s label from the dataset based on gradient boosting decision tree (GBDT) algorithm and VGGNet, which is flexible to the changing dataset. More than 8 000 vehicle images are labeled with their components’ information, such as headlights, windows, wheels, and logos. The YOLO network is employed to detect and localize the typical components of a vehicle. The vehicle postures are estimated by the spatial relationship between different segmented components. Due to the variety of the perspectives, a 7-dimensional vector is defined to represent the relative posture of the vehicle and screen out the images with a similar photographic perspective. Two algorithms are used to extract the features from each image patch: (1) the scale invariant feature transform (SIFT) combined with the bag-of-features (BoF) and (2) pre-trained deep neural network. The GBDT is applied to evaluate the weight of each component regarding its impact on VMR. The descriptors of each component are then aggregated to retrieve the best matching image from the database. The results showed its advantages in terms of accuracy (89.2%) and efficiency, demonstrating the vast potential of applying this method to large-scale vehicle model recognition.

Abstract In high‐frequency transit, providing real‐time crowding information (RTCI) is a potential way to promote passenger satisfaction and reduce negative crowding externalities, by assisting passengers in choosing less crowded vehicles. To make RTCI convincing and reliable, it is necessary to provide predictive RTCI, in which bus passenger load (BPL) prediction is the primary problem. This paper proposes a novel two‐stage BPL prediction method using automatic passenger counting (APC) data. The first stage is to predict short‐term passenger flows at stops based on an adaptive Kalman filter approach. Using the outputs from the first stage as well as other variables directly from APC data, the second stage is to predict BPL based on a support vector regression algorithm. Several methods from the existing literature are used as benchmarks to test the relative performance of the proposed method. An empirical study on bus line 1 in Suzhou, China shows that the proposed method outperforms all the benchmarks, and shows significant superiority over other methods for stops with sharp increases in BPL and for multi‐step ahead prediction. This study contributes to the limited literature on BPL prediction and lays the foundation for providing accurate and reliable predictive RTCI in the future.

The effectiveness of the variable speed limit (VSL) control is affected by the deployment locations of VSL signs. In this paper, a procedure was proposed to help determine the deployment location of VSL signs to reduce collision risks at freeway recurrent bottlenecks. The procedure is started from determining the hazardous recurrent bottleneck section by constructing the profile of the collision risks. The length of the hazardous section was determined according to the pre-selected threshold of crash risks. Various scenarios were considered for different lengths of VSL controlled sections with various densities of VSL signs. A modified cell transmission model (CTM) was used for modeling the traffic flow at the freeway bottlenecks under the VSL control. The VSL control factors were optimized by using the genetic algorithm. The safety effects of VSL were greatly affected by the placement of VSL signs. In general, the scenario with a longer VSL controlled section and more speed limit signs was more effective in reducing rear-end collision risks. The cost-benefit analyses showed that the placement of VSL signs in scenario C2 with the controlled section length of 11.7 mi and average VSL density of 1.0 mi had the best benefit/cost effect. Using 12 VSL signs, the collision risks were reduced by 69.16% and the total travel time was increased slightly by 7.73%. The procedure can help determine the optimal deployment of VSL signs on freeways, considering both the safety benefits and the cost.

Crash injury severity prediction is a promising research target in traffic safety. Traditionally, various statistical methods were used for modeling crash injury severities. In recent years, machine learningbased methods are becoming popular due to their good predictive performance. However, the machine learning-based models are usually criticized as they perform like a black-box. In this paper, we aim at comparing the predictive performance, including prediction accuracy and estimation of variable importance, among various machine learning and statistical methods with distinct modeling logic for crash severity analysis. The crash severity, road geometry, and traffic flow data were collected at freeway diverge areas in Florida. We estimated two most commonly used statistical methods which were ordered probit (OP) model and multinomial logit model, and four popular machine learning methods, including K-Nearest Neighbor, Decision Tree, Random Forest (RF), and Support Vector Machine. The correct prediction rate for each crash severity level and the overall correct prediction rate were calculated. The results showed that the machine learning methods had higher predicting accuracy than the statistical methods, though they suffered from overfitting issue. The RF method had the best prediction in overall and severe crashes while OP was the weakest one. We compared variable importance on crash severity via perturbation-based sensitivity analyses. The results showed that the inferences of variable importance from different methods were not always consistent and should be paid careful attention.

Nowadays phone distraction has started to become an increasingly recognized phenomenon. This article aims to examine the influences of phone use on pedestrian crossing behavior at signalized intersections in China. Using video recording and manual counting, pedestrian crossing behavior, age, gender, phone use, and waiting time are obtained at four signalized intersections. Totally, 4196 pedestrians are observed in four peak hours. Among them, 328 pedestrians (7.82%) are using their mobile phones, including 162 male pedestrians and 166 female pedestrians. The average phone use rate in different age groups are presented as follows: children (4.49%), youth (10.69%), middle-aged (6.87%), and elderly (1.15%). In terms of the phone using behavior on the crosswalk, age is a significant factor, while gender is not. For the type of violation behavior, the results show that pedestrians who use mobile phones are most likely to be late starters. In addition, some other important results are found: mobile phone use can raise the violation of pedestrian crossing behavior; pedestrians using their phones are more likely to cross on red; and pedestrians using their phones while crossing walk more slowly. Finally, the application significance of this study and some recommendations are provided to improve pedestrian safety.

Old town fringe area refers to a surrounding zone of an old town. The fringe undertakes the important function of evacuating traffic volume between suburb districts and central city. Traffic flow and travel behavior of the old town fringe are complex and different from other areas. Thus, recognition of fringe area is of importance for researchers to better understand the unique travel features and propose proper policies for fringe renewal. This study took the city of Nanjing as an example and fused the residents’ travel survey data and the point-of-interest data for fringe recognition. The study estimated the travel intensity of each travel analysis zone per day. The mutation point of travel intensity was decided to divide the fringe. The point-of-interest data was used for validating the boundaries of the fringe. The fusion of the two data sets jointly decided the core old town fringe area. The travel behavior characteristics of the fringe area, including the fringe internal trips, crossing fringe trips, and those with only origin or destination in the fringe, were then evaluated and policy suggestions were provided. The findings of the study will benefit the urban space planning and coordinated transportation system development in the fringe area of old towns.

The main purpose of this article is to compare the mode choice decisions between commuting and non-commuting trip chains and evaluate the uncertainties in the mode choice process. According to household survey data from a medium-sized city in China, the whole day of travel is divided into several types of travel chains. We used the multinomial logit model to estimate the impacts of factors on the choices of trip modes, which included walk, bike, public transit, and car. The entropy theory was introduced to evaluate the uncertainty of each traveler’s mode choice decision. The results indicate that there are great differences in mode choice between commuting and non-commuting trip chains. It is found that the causes and effects of different chains are different. The results help to understand the decision-making process of mode.

Transit signal priority (TSP) is a promising low-cost strategy that gives preferential treatments for the buses to go through intersections with minimum delay time. In this paper, a new TSP control model was presented for isolated intersections to minimize bus delay and to reduce the impact of TSP on other vehicles by optimizing signal control phase selection and compression. This paper starts with the phase selection and compression strategies to provide treatments to bus priority requests. Then, two new features on phase selection and compression aspects are applied to TSP, i.e. the time that a bus priority request needs is provided by the phase(s) with the lowest traffic volume, and multi-phases can be selected to serve a bus request. Field data are collected from a major traffic corridor in Changzhou (China) and applied for VISSIM simulation. The proposed TSP control model as well as the fixed-time control and the conventional TSP control models are tested and compared under different traffic demands, headways and maximum saturation degrees. The comparative results showed that the proposed model outperformed the conventional TSP control model in terms of reducing bus delay, minimizing the impact on other vehicles and reducing the stop rate for buses. This paper reveals that, the proposed TSP strategy can significantly optimize the phase compression process and improve transit efficiency.

Many urban streets are designed with on-street bike lanes to provide right-of-way for bicycle traffic. However, when bicycle flow is large, extensive passing maneuvers could occupy vehicle lanes and thus cause interferences to vehicle traffic. The primary objective of this study is to evaluate how bicycle traffic affects vehicle operation on urban streets with bike lanes. Data were collected on six street segments in Nanjing, China. The cumulative curves were constructed to extract traffic flow information including individual bicycle and vehicle speeds and aggregated traffic parameters such as flow and density. The results showed that as bicycle density on bike lanes continuously increases faster bicycles may run into vehicle lanes causing considerable reductions in vehicle speeds. A generalized linear model was estimated to predict the vehicle delay. Results showed that vehicle delay increases as bicycle flow and vehicle flow increase. Number of vehicle lanes and width of bike lane also have significant impact on vehicle delay. Findings of the study are helpful to regions around the world in bike infrastructure design in order to improve operations of both bicycles and vehicles.