Yuanchang Xie

Analysis of driver injury severity in rural single-vehicle crashes

Rural roads carry less than fifty percent of the traffic in the United States. However, more than half of the traffic accident fatalities occurred on rural roads. This research focuses on analyzing injury severities involving single-vehicle crashes on rural roads, utilizing a latent class logit (LCL) model. Similar to multinomial logit (MNL) models, the LCL model has the advantage of not restricting the coefficients of each explanatory variable in different severity functions to be the same, making it possible to identify the impacts of the same explanatory variable on different injury outcomes. In addition, its unique model structure allows the LCL model to better address issues pertinent to the independence from irrelevant alternatives (IIA) property. A MNL model is also included as the benchmark simply because of its popularity in injury severity modeling. The model fitting results of the MNL and LCL models are presented and discussed. Key injury severity impact factors are identified for rural single-vehicle crashes. Also, a comparison of the model fitting, analysis marginal effects, and prediction performance of the MNL and LCL models are conducted, suggesting that the LCL model may be another viable modeling alternative for crash-severity analysis.

A Wavelet Network Model for Short-Term Traffic Volume Forecasting

Wavelet networks (WNs) are recently developed neural network models. WN models combine the strengths of discrete wavelet transform and neural network processing to achieve strong nonlinear approximation ability, and thus have been successfully applied to forecasting and function approximations. In this study, two WN models based on different mother wavelets are used for the first time for short-term traffic volume forecasting. The Levenberg-Marquardt algorithm is used to train the WN models because it has better efficiency than the other algorithms based on gradient descent. Using the traffic volume data collected on Interstate 80 in California, the WN models are compared with the widely used back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models. The performance evaluation is based on mean absolute percentage error (MAPE) and variance of absolute percentage error (VAPE). The test and comparison results show that the WN models consistently produce lower average MAPE and VAPE values than the BPNN and RBFNN models, suggesting that the WN models are a better predictor of accuracy, stability, and adaptability.

Forecasting of Short-Term Freeway Volume with v-Support Vector Machines

Predictions for short-term traffic volume provide important inputs for traveler information and traffic management. Traffic volumes in the near future are often estimated based on historical volumes. Because of the complicated nonlinear relationship between historical and future traffic volume data, many previous studies used neural networks to predict short-term traffic volumes. In this research, a v-support vector machine (v-SVM) model, which has the particular strength of overcoming local minima and overfitting common to neural network models, is proposed for short-term traffic volume prediction. The v-SVM model is compared with a widely used multilayer feed-forward neural network (MLFNN) model using four data sets collected from three interstate freeways. Testing results show that for both one-step and two-step forecasting, the v-SVM model outperforms the MLFNN model for all data sets in terms of mean absolute percentage error and root-mean-square error. Key issues in applying both models are also discussed in this article.

A multimodal location and routing model for hazardous materials transportation

The recent US Commodity Flow Survey data suggest that transporting hazardous materials (HAZMAT) often involves multiple modes, especially for long-distance transportation. However, not much research has been conducted on HAZMAT location and routing on a multimodal transportation network. Most existing HAZMAT location and routing studies focus exclusively on single mode (either highways or railways). Motivated by the lack of research on multimodal HAZMAT location and routing and the fact that there is an increasing demand for it, this research proposes a multimodal HAZMAT model that simultaneously optimizes the locations of transfer yards and transportation routes. The developed model is applied to two case studies of different network sizes to demonstrate its applicability. The results are analyzed and suggestions for future research are provided.

Review of Microscopic Lane-Changing Models and Future Research Opportunities

Driver behaviors, particularly lane-changing behaviors, have an important effect on the safety and throughput of the roadway-vehicle-based transportation system. Lane-changing models are a vital component of various microscopic traffic simulation tools, which are extensively used and playing an increasingly important role in Intelligent Transportation Systems studies. The authors conducted a detailed review and systematic comparison of existing microscopic lane-changing models that are related to roadway traffic simulation to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies. Many models have been developed in the last few decades to capture the uncertainty in lane change modeling; however, lane-changing behavior in the real world is very complex due to driver distraction (e.g., texting and cellphone or smartphone use) and environmental (e.g., pavement and lighting conditions) and geometric (e.g., horizontal and vertical curves) factors of the roadway, which have not been adequately considered in existing models. Therefore, large and detailed microscopic vehicle trajectory data sets are needed to develop new lane changing models that address these issues, and to calibrate and validate lane-changing models for representing the real world reliably. Possible measures to improve the accuracy and reliability of lane-changing models are also discussed in this paper.

Crash Frequency Analysis with Generalized Additive Models

Recent crash frequency studies have been based primarily on generalized linear models, in which a linear relationship is usually assumed between the logarithm of expected crash frequency and other explanatory variables. For some explanatory variables, such a linear assumption may be invalid. It is therefore worthwhile to investigate other forms of relationships. This paper introduces generalized additive models to model crash frequency. Generalized additive models use smooth functions of each explanatory variable and are very flexible in modeling nonlinear relationships. On the basis of an intersection crash frequency data set collected in Toronto, Canada, a negative binomial generalized additive model is compared with two negative binomial generalized linear models. The comparison results show that the negative binomial generalized additive model performs best for both the Akaike information criterion and the fitting and predicting performance.

Crash frequency analysis of different types of urban roadway segments using generalized additive model.

INTRODUCTION This paper utilizes generalized additive model to explore the potential non-linear relationship between crash frequency and exposure on different types of urban roadway segments. METHODS Generalized additive models are used to analyze crash frequency data and compared with the commonly used crash rate method and generalized linear models using a five-year crash data set from Houston, Texas. RESULTS The study shows that the relationship between crash frequency and exposure varies by segment type and the linearity may only approximately exist in certain segment types. In addition, the generalized additive modeling results suggest that such relationship curves may not be monotonic. Finally, this study demonstrates that generalized additive models in general provide better flexibility and modeling performance than generalized linear models. IMPACT ON INDUSTRY The generalized additive model provides a very promising alternative for crash frequency modeling and other safety studies.

Travel Mode Choice Modeling with Support Vector Machines

This study investigates the applications of nontraditional models for travel mode choice modeling, which traditionally has relied on disaggregate discrete choice models such as multinomial logit models. A new artificial intelligence model, a support vector machine, is applied for the first time to travel mode choice modeling. This support vector machine model is tested and compared with a multinomial logit model and a multilayer feedforward neural network model based on data collected in the San Francisco Bay Area in California. Two scenarios with different training data sizes are tested. For both scenarios, the support vector machine model outperforms the multinomial logit model in terms of fitting and testing results. Although the multilayer feedforward neural network model performs best for fitting, it underperforms the other two models for testing. It is recommended that the support vector machine model be used as an alternative procedure for travel mode choice modeling because of its promising performance and easy implementation.

Gaussian Processes for Short-Term Traffic Volume Forecasting

The accurate modeling and forecasting of traffic flow data such as volume and travel time are critical to intelligent transportation systems. Many forecasting models have been developed for this purpose since the 1970s. Recently kernel-based machine learning methods such as support vector machines (SVMs) have gained special attention in traffic flow modeling and other time series analyses because of their outstanding generalization capability and superior nonlinear approximation. In this study, a novel kernel-based machine learning method, the Gaussian processes (GPs) model, was proposed to perform short-term traffic flow forecasting. This GP model was evaluated and compared with SVMs and autoregressive integrated moving average (ARIMA) models based on four sets of traffic volume data collected from three interstate highways in Seattle, Washington. The comparative results showed that the GP and SVM models consistently outperformed the ARIMA model. This study also showed that because the GP model is formulated in a full Bayesian framework, it can allow for explicit probabilistic interpretation of forecasting outputs. This capacity gives the GP an advantage over SVMs to model and forecast traffic flow.

Collaborative merging strategy for freeway ramp operations in a connected and autonomous vehicles environment

ABSTRACT In a connected vehicle environment, vehicles are able to communicate and exchange detailed information such as speed, acceleration, and position in real time. Such information exchange is important for improving traffic safety and mobility. This allows vehicles to collaborate with each other, which can significantly improve traffic operations particularly at intersections and freeway ramps. To assess the potential safety and mobility benefits of collaborative driving enabled by connected vehicle technologies, this study developed an optimization-based ramp control strategy and a simulation evaluation platform using VISSIM, MATLAB, and the Car2X module in VISSIM. The ramp control strategy is formulated as a constrained nonlinear optimization problem and solved by the MATLAB optimization toolbox. The optimization model provides individual vehicles with step-by-step control instructions in the ramp merging area. In addition to the optimization-based ramp control strategy, an empirical gradual speed limit control strategy is also formulated. These strategies are evaluated using the developed simulation platform in terms of average speed, average delay time, and throughput and are compared with a benchmark case with no control. The study results indicate that the proposed optimal control strategy can effectively coordinate merging vehicles at freeway on-ramps and substantially improve safety and mobility, especially when the freeway traffic is not oversaturated. The ramp control strategy can be further extended to improve traffic operations at bottlenecks caused by incidents, which cause approximately 25% of traffic congestion in the United States.