Connected vehicle technology provides promising opportunities to improve road safety, enhance traffic efficiency, and reduce fuel consumption and emissions. It has been suggested that if drivers comply with suggested recommendations, connected vehicle technology can introduce huge benefits. However, whether drivers will accept suggestions and what factors will influence their likelihood of accepting the suggestions in a connected environment have not been studied. In addition, few models have been developed to predict drivers' reactions under such conditions. This paper aims to fill the research gap by examining and modeling drivers' acceptance and behavior when receiving energy- and safety-related speed recommendations through vehicle-to-infrastructure communications. A mixed-subject-design experiment was conducted in a closed-loop test track, Mcity, with seven intersection maneuver scenarios. A generally high compliance rate to the recommended speed strategies was observed that during 72% of the events, drivers changed their intersection-approaching behavior to follow the recommendations. Mixed models were conducted to explore the impacting factors while Principal Component Analysis was used to classify subjective (i.e., self-reported) data into four categories. To predict drivers' reactions when offered a speed suggestion, Random Forests were built with 13 independent variables, derived from four categories: vehicle kinematic features, device information, driver characteristics, and subjective data. Using this model, drivers' reactions during each intersection maneuver could be predicted with a reasonably high accuracy about 87.4 m away from the intersection, where the vehicle started to receive signal phase and timing information. Findings in this study can contribute to the optimization of energy-saving algorithms and the improvement of driving safety by using connected vehicle technologies.
Accurate and robust short-term traffic prediction is an important part of advanced traveler information systems. With the development of intelligent navigation and autonomous driving, it is necessary to explore the lane-level predictions of traffic speeds. However, most existing traffic prediction models concentrate on forecasting the traffic flow characteristics of the entire road sections rather than those of certain lanes. This paper proposes a fusion deep learning (FDL) model to predict lane-level traffic speed. First, the entropy-based grey relation analysis is introduced to choose lane sections that are strongly correlated with the lane section to be predicted. Second, a two-layer deep learning framework is established by combining the long short-term memory (LSTM) neural network and the gated recurrent unit (GRU) neural network. Third, the ground-truth data of several lane sections captured by remote traffic microwave sensors (RTMS) on the 2nd Ring Road of Beijing are utilized to examine the FDL model and compare it with several benchmark models. The experimental result indicates that in addition to capturing the fluctuations of traffic speed at the lane level, the FDL model has better performance than the benchmark models in terms of prediction accuracy and stability.
Traffic crash hot spot analyses allow identification of roadway segments that may be of safety concern. Understanding geographic patterns of existing motor vehicle crashes is one of the primary steps for geostatistical-based hot spot analysis. Much of the current literature, however, has not paid particular attention to differentiating among cluster types based on crash severity levels. This study aims at building a framework for identifying significant spatial clustering patterns characterized by crash severity and analyzing identified clusters quantitatively. A case study using an integrated method of network-based local spatial autocorrelation and the Kernel density estimation method revealed a strong spatial relationship between crash severity clusters and geographic regions. In addition, the total aggregated distance and the density of identified clusters obtained from density estimation allowed a quantitative analysis for each cluster. The contribution of this research is incorporating crash severity into hot spot analysis thereby allowing more informed decision making with respect to highway safety.
The paper provides information relevant to the development of driver and pedestrian safety systems by examining drivers' responses to infrastructure-based safety messages (DII) with a redundant in-vehicle display component. A driving simulator was used to create a conflict situation which required an immediate driver response to avoid a collision. At the start of the event, a pedestrian was occluded by a truck at an intersection. Partway through the event, the pedestrian dashed into the road and into the driver's path. When redundant visual in-vehicle alerting messages were provided, drivers released the throttle more quickly, engaged the brake more quickly, and had longer minimum time to collision relative to the baseline condition which lacked visual alerts. This was an improvement over the DII-only condition, where drivers did not brake more quickly relative to baseline and had only marginally longer minimum time to collision compared with the baseline condition. The findings suggest that redundant in-vehicle message information provides a benefit to many drivers over systems that use only infrastructure-based safety systems in vehicle-pedestrian conflicts.
Taxation in the aviation industry has evolved considerably over the last 25 years. Despite the vital role aviation and airports play in efficiently moving goods and people, the effect of taxation in this industry is understudied. Understanding how passengers and carriers respond to taxes and government fees is crucial to efficiently raising government revenue. After an overview of how taxation has evolved in the industry, this paper estimates how fares adjust in response to tax changes. Exploiting variation in taxes across similar routes and over time, the results suggest taxes are over-shifted to consumers (i.e., a $1 increase in taxes results in more than a $1 increase in the total fare). The paper discusses potential explanations for this result: the nature of competition in the industry and the propagation of taxes within a network.
Reliability is a key determinant of the quality of a transit service. Control is needed to deal with the stochastic nature of high-frequency bus services and to improve service reliability. This study focuses on holding control, both schedule- and headway-based strategies. An assessment framework is developed to systematically assess the effect of different strategies on passengers, the operator, and the transport authority. This framework can be applied by operators or authorities to determine which holding strategy is most beneficial to regulate headways, and thus solve related problems. In this research knowledge is gained about what service characteristics affect the performance of holding strategies and the robustness of these strategies in disrupted situations, by using scenarios. The framework is applied to a case study of a high-frequency regional bus line in the Netherlands. Based on the simulation results, the study identified the line characteristics that are important for the performance of schedule- and headway-based strategies and determined how robust different strategies are in the case of disruptions. Headway-based control strategies better mitigate irregularity along the line, especially when there are disruptions. However, schedule-based control strategies are currently easier to implement, because they do not require large changes in practice, and the performance of both strategies is generally equal in regular, undisrupted situations. In this paper, insights into what the concerns are for operators with respect to technical adaptations, logistical changes, and behavioral aspects when using a headway-based strategy are given.
Crash severity is one of the most widely studied topics in traffic safety area. Scholars have studied crash severity through various types of models. Using the publicly available 2017 Maryland crash data from the Department of Maryland State Police, the authors develop a multinomial logit (MNL) model and a random forest (RF) model, which belong to discrete choice and tree-based models, respectively, to (1) identify factors contributing to crash severity and (2) compare prediction performances and interpretation abilities between the two models. Based on the model results, major contributing factors of crash severity are identified, including collision type, occupant age, and speed limit. For the given dataset, RF has a higher prediction accuracy than MNL based on multiple measures (precision, recall, and F-1 score), even though the differences are not dramatic. Sensitivity analysis results show that RF is less sensitive than MNL. RF can automatically capture the non-linear effects of continuous variables and reduce the influence of collinearity relationships existing among explanatory variables. This study shows the possibility of conducting sensitivity analysis to enhance understanding of MNL and RF results, and uncovers unique characteristics of the discrete choice and tree-based models.
Roadside LiDAR deployment provides a solution to obtain the real-time high-resolution micro traffic data of unconnected road users for the connected-vehicle road network. Single roadside LiDAR sensor has a lot of limitations considering the scant coverage and the difficulty of handling object occlusion issue. Multiple roadside LiDAR sensors can provide a larger coverage and eliminate the object occlusion issue. To combine different LiDAR sensors, it is necessary to integrate the point clouds into the same coordinate system. The existing points registration methods serving mapping scans or autonomous sensing systems could not be directly used for roadside LiDAR sensors considering the different feature of point clouds and the spare points in the cost-effective roadside LiDAR sensors. This paper developed an approach for roadside LiDAR points registration. The developed points-aggregation-based partial iterative closest point algorithm (PA-PICP) is a semi-automatic points registration method, which contains two major parts: XY data registration and Z adjustment. A semi-automatic key point selection method was introduced. The partial iterative closest point was applied to minimize the difference between different LiDARs in the XY plane. The intersection of ground surface between different LiDARs was used for Z-axis adjustment. The performance of the developed procedure was evaluated with field-collected LiDAR data. The results showed the effectiveness and accuracy of data integration using PA-PICP was greatly improved compared with points registration using the traditional iterative closest point. The case studies also showed that the occlusion issue can be fixed after PA-PICP points registration.
This paper analyzes the design problem of a paired-line hybrid transit system in a monocentric city with a densely populated central business district (CBD). The trip production rate is assumed to decrease exponentially with increasing distance from the city center. As to the trip distribution, two different areas, the CBD and the rest of the city, are identified. Trips ending in each area are homogeneously distributed within that area but their proportions in the total trips are distinctive to model the heterogeneity in trip distribution. To address the challenge of estimating system costs analytically under the above exponential demand pattern, new approximation methods are proposed and validated using a Monte Carlo simulation. Results of numerical experiments show that the exponential demand pattern helps reduce the cost of paired-line hybrid transit systems in monocentric cities, with a saving up to 20% when both the trip production and distribution are heavily concentrated in the CBD. Furthermore, strong quadratic/linear relationships are found between the parameters controling demand concentration level and the system cost. The proposed model can guide the design of hybrid transit systems in monocentric cities with a demand pattern similarly structured as considered in this study.
The wide deployment of vehicle automation and communication systems (VACS) in the next decade is expected to influence traffic performance on freeways. Apart from safety and comfort, one of the goals is the alleviation of traffic congestion which is a major and challenging problem for modern societies. The paper investigates the combined use of two feedback control strategies utilizing VACS at different penetration rates, aiming to maximize throughput at bottleneck locations. The first control strategy employs mainstream traffic flow control using appropriate variable speed limits as an actuator. The second control strategy delivers appropriate lane-changing actions to selected connected vehicles using a feedback-feedforward control law. Investigations of the proposed integrated scheme have been conducted using a microscopic simulation model for a hypothetical freeway featuring a lane-drop bottleneck. The results demonstrate significant improvements even for low penetration rates of connected vehicles.
This paper explores the feasibility of further research into providing free public charging services. Charging behavior research has noted some increased usage of free charging facilities, but inferential analysis has not studied whether this increased usage is a result of decreased price solely or free is a special price. Recent research in other fields, such as behavioral economics and marketing, has analyzed whether people's decision process varies between when a product's price is "free" and when it has a positive price. Laboratory and field experiments have found that people treat zero as a special price and obtain additional benefit from purchasing a free product or obtaining a free product in a bundle. Because of a lack of data and study in the electric vehicle area, evaluating the effectiveness of free charging programs is currently difficult. This paper explores potential impacts of free charging on plug-in electric vehicle adoption by integrating empirical findings in marketing research with consumer choice modeling. A behavioral framework is proposed to represent the perceived benefit from free charging events. The Market Acceptance of Advanced Automotive Technologies (MA3T) model was modified to incorporate a value of free from free charging events. Results show that a short-term free public charging program could possibly increase plug-in electric vehicle sales, decrease oil consumption, and decrease greenhouse gas emissions at an efficient cost-benefit rate. This exploratory work motivates further study and the paper concludes with a discussion of research programs that could be used to determine a valuation for free charging.
Riprap rock and large-sized aggregates have been used extensively in geotechnical and hydraulic engineering. They essentially provide erosion control, sediment control, and scour protection. The sustainable and reliable use of riprap materials demands efficient and accurate evaluation of their large particle sizes, shapes, and gradation information at both quarry production lines and construction sites. Traditional methods for assessing riprap geometric properties involve subjective visual inspection and time-consuming hand measurements. As such, achieving the comprehensive in-situ characterization of riprap materials still remains challenging for practitioners and engineers. This paper presents an innovative approach for characterizing the volumetric properties of riprap by establishing a field imaging system associated with newly developed color image segmentation and three-dimensional (3-D) reconstruction algorithms. The field imaging system described in this paper with its algorithms and field application examples is designed to be portable, deployable, and affordable for efficient image acquisition. The robustness and accuracy of the image segmentation and 3-D reconstruction algorithms are validated against ground truth measurements collected in stone quarry sites and compared with state-of-the-practice inspection methods. The imaging-based results show good agreement with the ground truth and provide improved volumetric estimation when compared with currently adopted inspection methods. Based on the findings of this study, the innovative imaging-based system is envisioned for full development to provide convenient, reliable, and sustainable solutions for the onsite Quality Assurance/Quality Control tasks relating to riprap rock and large-sized aggregates.
Large-scale natural disasters challenge the resilience of the surface transportation system. The objective of this research was to develop a resilience model of the surface transportation system with a mixed-traffic environment and considering varying Connected and Automated Vehicle (CAV) penetration scenarios. As deployment of CAVs is expected to improve traffic operations, a resilience model was developed in this research to evaluate the resilience performance of a transportation system with several CAV penetration levels (0%, 25%, 50%, 75%, and 100%) for a given budget and recovery time. The proposed resilience quantification model was applied on a roadway network considering several disaster scenarios. The network capacity in relation to trips at any phase of disaster was compared with the pre-disaster trips to determine the system resilience. The capacity variation and the travel time variation were also estimated. The analysis showed that the resilience of the transportation system improved with CAVs in relation to travel time and capacity improvement. Link travel times were significantly improved by higher CAV penetration rate. The findings also suggested that higher penetration of CAVs (i.e., 50% or more) increased the recovery costs. For example, the recovery costs needed for medium and large-scale disasters were 50% and 90% higher, respectively, compared with the recovery costs for a small-scale disaster. These higher costs were primarily for the repair and replacement of intelligent infrastructure required to support the operation of CAVs.
Increasing electric vehicle (EV) shares and fuel economy pose challenges to a fuel tax-based transportation funding scheme. This paper evaluates such fuel tax revenue impacts using Virginia as a case study. First, a bivariate count model is developed using vehicle registration data in 132 counties from 2012 to 2016. Model results indicate strong correlation between presence of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on a county basis. Counties with higher percent of males are associated with higher BEV (but not PHEV) counts. In contrast, higher average commute time is predicted to increase the number of PHEVs in each county, but not BEVs. Greater population density, population over 65, population with graduate degrees, and household size are found to increase PHEV and BEV counts, whereas more households with children is associated with fewer EVs. The analysis forecasts 0.6-10% statewide EV adoption by 2025, with an adoption rate of 2.4% in the most likely scenario. Nine scenarios, combining different predictions of EV adoption and fuel economy improvement, project 2025 statewide fuel tax revenue to decrease by 5-19%, relative to 2016 receipts. Furthermore, model results suggest that, on average, a light-duty vehicle in a rural area will pay 28% more in fuel taxes than its urban counterpart by 2025. The framework proposed here provides a reference for other regions to conduct similar analysis using public agency data in the vehicle electrification era.
This paper discusses the process used to develop a safety improvement plan for unsignalized intersections using systemic low-cost countermeasures. The scope of this project focused on unsignalized intersections with stop sign control on the minor approaches. The first objective was to perform an assessment of Virginia's unsignalized intersection crashes over a five-year period to determine predominant crash trends and collision types to target for treatment. The four focus collision types with the highest frequency of crashes and the greatest potential reduction in crashes were 3-leg angle, 3-leg fixed object off the road, 4-leg angle and 4-leg rear end. Chi-square automatic interaction detection decision tree analysis was used to perform a systemic analysis to identify a group of intersections associated with potential risk factors related to the focus collision types. A tiered list of systemic countermeasures to deploy was developed. The countermeasures were intended to warn of the stop ahead, make the stop sign and stop location more visible on a minor street, and to warn of the intersection ahead on a major street. The potential for safety improvement measure was used to prioritize the candidate treatment intersections. Before deployment, a study of the intersection by district traffic engineering staff was planned to finalize the plan. The output from the research was a safety improvement plan to systemically deploy treatments to unsignalized intersections as part of the safety program.