Browsing by Author "Liu, Hongchao (TTU)"
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Item Factor identification and prediction for teen driver crash severity using machine learning: A case study(2020) Lin, Ciyun; Wu, Dayong; Liu, Hongchao (TTU); Xia, Xueting (TTU); Bhattarai, Nischal (TTU)Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads inWest Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety inWest Texas and other rural areas with similar issues.Item Modeling and analyzing taxi congestion premium in congested cities(2017) Yuan, Changwei; Wu, Dayong (TTU); Wei, Dali; Liu, Hongchao (TTU)Traffic congestion is a significant problem in many major cities. Getting stuck in traffic, the mileage per unit time that a taxicab travels will decline significantly. Congestion premium (or so-called low-speed fare) has become an increasingly important income source for taxi drivers. However, the impact of congestion premium on the taxicab market is not widely understood yet. In particular, modeling and analyzing of the taxi fare structure with congestion premium are extremely limited. In this paper, we developed a taxi price equilibrium model, in which the adjustment mechanism of congestion premium on optimizing the taxi driver’s income, balancing the supply and demand, and eventually improving the level of service in the whole taxicab market was investigated. In the final part, we provided a case study to demonstrate the feasibility of the proposed model. The results indicated that the current taxi fare scheme in Beijing is suboptimal, since the gain from the raise of congestion premium cannot compensate for the loss from the demand reduction. Conversely, the optimal fare scheme suggested by our model can effectively reduce the excessive demand and reach the supply-demand equilibrium, while keeping the stability of the driver’s income to the maximum extent.Item Optimize Evacuation Route Considering the Operational Cost as a Constraint(2013) Xu, Hao (TTU); Liu, Hongchao (TTU)Evacuation planning involves modeling, operation, and management of evacuation routes before and during emergency evacuation. This paper presents the development and examination of a route optimization model which considers the operational cost as a constraint. The study is focused on the situation when the backbone evacuation route, usually the interstate highway system, is over congested and some traffic need to be directed to local street networks. As intersections are the key control components of a street network, traffic operations at intersections cannot be neglected in route selection. In order to consider the operational cost in the modeling process, a street network is first presented by an extended graph network which has cost information associated with each node. Then, a system optimal traffic assignment approach is used to select and optimize the evacuation routes. The property of the approach is examined by a numerical test which includes a network composed of a backbone interstate highway and a grid network with over 30 intersections.Item Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data(2020) Zhang, Qi; Chen, Hong; Liu, Hongchao (TTU); Li, Wei; Zhang, Yibin (TTU)Origin-destination- (O-D-) based travel time reliability (TTR) is fundamental to next-generation navigation tools aiming to provide both travel time and reliability information. While previous works are mostly focused on route-based TTR and use either ad hoc data or simulation in the analyses, this study uses open-source Uber Movement and Weather Underground data to systematically analyze the impact of rainfall intensity on O-D-based travel time reliability. The authors classified three years of travel time data in downtown Boston into one hundred origin-destination pairs and integrated them with the weather data (rain). A lognormal mixture model was applied to fit travel time distributions and calculate the buffer index. The median, trimmed mean, interquartile range, and one-way analysis of variance were used for quantification of the characteristics. The study found some results that tended to agree with the previous findings in the literature, such that, in general, rain reduces the O-D-based travel time reliability, and some seemed to be unique and worthy of discussion: firstly, although in general the reduction in travel time reliability gets larger as the intensity of rainfall increases, it appears that the change is more significant when rainfall intensity changes from light to moderate but becomes fairly marginal when it changes from normal to light or from moderate to extremely intensive; secondly, regardless of normal or rainy weather, the O-D-based travel time reliability and its consistency in different O-D pairs with similar average travel time always tend to improve along with the increase of average travel time. In addition to the technical findings, this study also contributes to the state of the art by promoting the application of real-world and publicly available data in TTR analyses.Item Probabilistic Prediction of Pedestrian Crossing Intention Using Roadside LiDAR Data(2019) Zhao, Junxuan (TTU); Li, Yinfeng; Xu, Hao; Liu, Hongchao (TTU)Pedestrians are vulnerable road users that need proactive protection. While both autonomous and connected vehicle technologies aim to deliver greater safety benefits, current designs heavily rely on vehicle-based or on-board sensors and lack strategic real-time interactions with pedestrians who do not have any communication means. As pedestrians are passively protected by the system, they might be put into hazardous situations when vehicle-mounted sensors fail to detect their presence. This paper is part of ongoing research that uses roadside light detection and ranging (LiDAR) sensors to develop a human-in-the-loop system that brings pedestrians into the connected environment. To proactively protect pedestrians, accurate prediction of their intention for crossings at locations, such as unsignalized intersections and street mid-blocks is critical, and this paper presents a modified Naïve Bayes approach for this purpose. It features a probabilistic approach to overcoming the common deficiencies in deterministic methods and provides valuable comparisons between feature-based data processing methods, such as artificial neural network (ANN) and model-based Naïve Bayes approach. A case study was conducted by using a low-cost 16-line LiDAR sensor installed at the roadside. Pedestrians' crossing intention was predicted at a range of 0.5-3 s before actual crossings. The results satisfactorily demonstrated the properties of the modified Naïve Bayes model, as well as its higher flexibility, compared with the ANN approaches in practice.Item Using grey relational analysis to evaluate energy consumption, CO2 emissions and growth patterns in China’s provincial transportation sectors(2017) Yuan, Changwei; Wu, Dayong (TTU); Liu, Hongchao (TTU)The transportation sector is a complex system. Collecting transportation activity and the associated emissions data is extremely expensive and time-consuming. Grey Relational Analysis provides a viable alternative to overcome data insufficiency and gives insights for decision makers into such a complex system. In this paper, we achieved three major goals: (i) we explored the inter-relationships among transportation development, energy consumption and CO2 emissions for 30 provincial units in China, (ii) we identified the transportation development mode for each individual province, and (iii) we revealed policy implications regarding the sustainable transportation development at the provincial level. We can classify the 30 provinces into eight development modes according to the calculated Grey Relational Grades. Results also indicated that energy consumption has the largest influence on CO2 emission changes. Lastly, sustainable transportation policies were discussed at the province level according to the level of economy, urbanization and transportation energy structure.