2007–2016 FATAL TRAFFIC CRASHES IN ALASKA, HAWAII, IDAHO, AND WASHINGTON AND CHARACTERISTICS OF TRAFFIC FATALITIES INVOLVING HAWAIIANS AND CSET MINORITIES

Data for this comparative study were collected from the Fatality Analysis and Reporting System (FARS) for the years 2007 to 2016 for the states of Alaska, Hawaii, Idaho, and Washington. The rates of roadway fatalities, especially those of American Indians (which include Aleuts and Eskimos), Guamanians, Samoans, and Native Hawaiians (which include part-Hawaiians) were the focus of the study; they are referred to as “CSET Minorities” in this report; all other races are referred to as “All Others.” Three main contributing factors for fatal crashes—alcohol use, speeding, and non-usage of restraint—were analyzed for each population group. CSET states are lagging behind many countries in terms of traffic safety. Significant differences in the involvement of alcohol, speeding, and non-usage of restraint were indicated between CSET Minority fatalities and All Others. For all types of crashes examined, CSET Minorities exhibited statistically significant differences, nearly all of them being higher or worse than All Others, except for motorcycle crashes. In Hawaii, the proportion of Hawaiians in the population is steady at approximately 21%, but their proportion in FARS database is at 28% and rising. Aggregate data analysis of traffic fatalities focused on three rural, indigenous, tribal, and isolated (RITI) communities in Hawaii, the entire Big Island of Hawaii, and the rural communities of Waianae and Waimanalo on the island of Oahu. All three locations are known for their relatively large number of Hawaiians and part-Hawaiians. The percentage of Hawaiians in traffic fatalities was 32% on the Big Island, 50% in Waianae, and 78% in Waimanalo

Evaluation of Delivery Service in Rural Areas with CAV

Urban areas have been experiencing automated delivery technology for several servings of food or a few bags of groceries, with automated (robotic) mini vehicles. The benefits of such automated delivery may be much more significant for rural areas with long distances due to the large potential savings in travel time, travel cost, and crash risk. Compared to urban areas, rural areas have older and more disabled residents, longer distances, higher traffic fatality rates, and high ownership of less fuel-efficient vehicles such as pickup trucks. An evaluation of connected autonomous vehicle (CAV) delivery service in rural areas was conducted. A detailed methodology was developed and applied to two case studies: One for deliveries between Hilo and Volcano Village in Hawaii as a case of deliveries over a moderate distance (~50-mile roundtrip) in a high-energy-cost environment, and another for deliveries between Spokane and Sprague in Washington State as a case of deliveries over a longer distance (~80-mile roundtrip) in a low-energy-cost environment. The delivery vehicles were based on the same compact van: A person-driven gasoline-powered van, a person-driven electric-powered van, and a CAV electric-powered van. The case study results suggest that the CAV van can be a viable option for implementing a delivery business for rural areas based on the evaluation results that accounted for a large number of location-specific costs and benefits and the number of orders served per trip

NATURALISTIC DRIVING DATABASE DEVELOPMENT AND ANALYSIS OF CRASH AND NEAR-CRASH TRAFFIC EVENTS IN HONOLULU

Dashboard cameras and sensors were installed in 233 taxi vans on Oahu, Hawaii which produced several hours of events classified as naturalistic driving data (NDD) in a period of seven months between fall 2019 and spring 2020. The study achieved its objectives to: (1) collect data from NDD events where driving maneuvers caused an acceleration of 0.5g or higher; (2) develop a database suitable for statistical analysis; (3) derive basic statistics for all variables; (4) investigate correlations between variables; and (5) further investigate correlations (which may represent causality effects) for the most frequent types of events, using stepwise linear regression models. The database included a total of 402 harsh events, of which were 398 near-crashes and four were crashes. Several variables such as road, environmental, driver and vehicle characteristics were coded for each event. The installation of Samsara by the CTL company proved to be a successful tool for coaching drivers, and for providing useful insights into traffic safety factors relating to near-miss events

Effects of Reading Text While Driving: A Driving Simulator Study

Although 47 US states make the use of a mobile phone while driving illegal, many people use their phone for texting and other tasks while driving. This research project summarized the large literature on distracted driving and compared major outcomes with those of our study. We focused on distraction due to reading text because this activity is most common. For this research project, we collected simulator observations of 203 professional taxi drivers (175 male, and 28 female) working at the same Honolulu taxi company, using the mid-range driving simulator VS500M by Virage. After a familiarization period, drivers were asked to read realistic text content relating to passenger pick up displayed on a 7-inch tablet affixed to the dashboard. The experimental scenario was simulated on a two-lane rural highway having a speed limit of 60 mph and medium traffic. Drivers needed to follow the lead vehicle under regular and text-reading conditions. The large sample size of this study provided a strong statistical base for driving distraction investigation on a driving simulator. The comparison between regular and text-reading conditions revealed that the drivers significantly increased their headway (20.7%), lane deviations (354%), total time of driving blind (352%), maximum duration of driving blind (87.6% per glance), driving blind incidents (170%), driving blind distance (337%) and significantly decreased lane change frequency (35.1%). There was no significant effect on braking aggressiveness while reading text. The outcomes indicate that driving performance degrades significantly by reading text while driving. Additional analysis revealed that important predictors for maximum driving blind time changes are sociodemographic characteristics, such as age and race, and past behavior attributes

Drone-based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring

This report documents the research activities to develop a drone-based computer vision-enabled vehicle dynamic safety performance monitoring in Rural, Isolated, Tribal, or Indigenous (RITI) communities. The acquisition of traffic system information, especially the vehicle speed and trajectory information, is of great significance to the study of the characteristics and management of the traffic system in RITI communities. The traditional method of relying on video analysis to obtain vehicle number and trajectory information has its application scenarios, but the common video source is often a camera fixed on a roadside device. In the videos obtained in this way, vehicles are likely to occlude each other, which seriously affects the accuracy of vehicle detection and the estimation of speed. Although there are methods to obtain high-view road video by means of aircraft and satellites, the corresponding cost will be high. Therefore, considering that drones can obtain high-definition video at a higher viewing angle, and the cost is relatively low, we decided to use drones to obtain road videos to complete vehicle detection. In order to overcome the shortcomings of traditional object detection methods when facing a large number of targets and complex scenes of RITI communities, our proposed method uses convolutional neural network (CNN) technology. We modified the YOLO v3 network structure and used a vehicle data set captured by drones for transfer learning, and finally trained a network that can detect and classify vehicles in videos captured by drones. A self-calibrated road boundary extraction method based on image sequences was used to extract road boundaries and filter vehicles to improve the detection accuracy of cars on the road. Using the results of neural network detection as input, we use video-based object tracking to complete the extraction of vehicle trajectory information for traffic safety improvements. Finally, the number of vehicles, speed and trajectory information of vehicles were calculated, and the average speed and density of the traffic flow were estimated on this basis. By analyzing the acquiesced data, we can estimate the traffic condition of the monitored area to predict possible crashes on the highways

Extracting Rural Crash Injury and Fatality Patterns Due to Changing Climates in RITI Communities Based on Enhanced Data Analysis and Visualization Tools (Phase I)

Traffic crashes cause considerable incapacitating injuries and losses in Rural, Isolated, Tribal, or Indigenous (RITI) communities. Compared to urban traffic crashes, those rural crashes, especially for those occurred in RITI communities, are heavily associated with factors such as speeding, low safety devices application (for instance, seatbelt), adverse weather conditions and lacking maintenance and repairers for road conditions, inferior lighting conditions, and so on. Therefore, there exists an urgent need to investigate the unique attributes associated with the RITI traffic crashes based on numerous approaches, such as statistical methods, and data-driven approaches. This project focused on extracting rural crash injury and fatality patterns due to changing climates in RITI communities based on enhanced data analysis and visualization tools. Three new interactive graphic tools were added to the Rural Crash Visualization Tool System (RCVTS), to enhance the visualization approach. A Bayesian vector auto-regression based data analysis approach was proposed to enable irregularly-spaced mixture-frequency traffic collision data interpretation with missing values. Moreover, a finite mixture random parameters model was formulated to explore driver injury severity patterns and causes in low visibility related single-vehicle crashes. The research findings are helpful for transportation agencies to develop cost-effective countermeasures to mitigate rural crash severities under extreme climate and weather conditions and minimize the rural crash risks and severities in the States of Alaska, Washington, Idaho, and Hawaii

Developing an Interactive Baseline Data Platform for Visualizing and Analyzing Rural Crash Characteristics in RITI Communities

This project focused on developing an interactive baseline crash data platform, termed as Rural Crash Visualization Tool System (RCVTS), to visualize and analyze rural crash characteristics in RITI communities. More than 975 thousand crash records were collected in the state of Alaska, Idaho, and Washington, from 2010 to 2016. Data fusion is applied to unify the collected data. In the proposed RCVTS platform, three main functions are defined: crash data visualization, data analysis, and data retrieval. Crash data visualization includes an on-street map based crash location tool and a graphic query tool. Data analysis involves a number of visualization approaches, including static charts— i.e., the scatter chart—the line chart, the area chart, the bar chart, and interactive graph— i.e., the sunburst chart. Users are allowed to generate customized analytical graphs by specifying the parameters and scale. The three types of authorized users are defined to download crash information in the data retrieval section following corresponding limitations. The proposed RCVTS was illustrated using a sample case with crash records of the State of Alaska. It showed that the proposed RCVTS functions well. Recommendations on future research are provided as well

Drone-Based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring

This report documents the research activities to develop a drone-based computer vision-enabled vehicle dynamic safety performance monitoring in Rural, Isolated, Tribal, or Indigenous (RITI) communities. The acquisition of traffic system information, especially the vehicle speed and trajectory information, is of great significance to the study of the characteristics and management of the traffic system in RITI communities. The traditional method of relying on video analysis to obtain vehicle number and trajectory information has its application scenarios, but the common video source is often a camera fixed on a roadside device. In the videos obtained in this way, vehicles are likely to occlude each other, which seriously affects the accuracy of vehicle detection and the estimation of speed. Although there are methods to obtain high-view road video by means of aircraft and satellites, the corresponding cost will be high. Therefore, considering that drones can obtain high-definition video at a higher viewing angle, and the cost is relatively low, we decided to use drones to obtain road videos to complete vehicle detection. In order to overcome the shortcomings of traditional object detection methods when facing a large number of targets and complex scenes of RITI communities, our proposed method uses convolutional neural network (CNN) technology. We modified the YOLO v3 network structure and used a vehicle data set captured by drones for transfer learning, and finally trained a network that can detect and classify vehicles in videos captured by drones. A self-calibrated road boundary extraction method based on image sequences was used to extract road boundaries and filter vehicles to improve the detection accuracy of cars on the road. Using the results of neural network detection as input, we use video-based object tracking to complete the extraction of vehicle trajectory information for traffic safety improvements. Finally, the number of vehicles, speed and trajectory information of vehicles were calculated, and the average speed and density of the traffic flow were estimated on this basis. By analyzing the acquiesced data, we can estimate the traffic condition of the monitored area to predict possible crashes on the highways

Associations of personality characteristics with transport behavior and residence location decisions

The objective of this paper is to investigate potential associations between personality and individual travel behavior characteristics. The explorations were based on responses to a mailback household survey from individuals residing in selected Chicago suburbs conducted in spring 1989. Three dimensions of personality were examined: social introversion or extroversion, affinity for suburban living and affinity for material possessions. Personality characteristics tend to correlate well with residence location selection, automobile ownership and travel characteristics. Specifically, socially extroverted people tend to make more trips, more nonwork trips and travel substantially longer distances by automobile for nonwork trips compared with socially introverted people. Materialistic people tend to spend a larger portion of their income for automobile acquisition; they also tend to own more expensive automobiles compared with utilitarian people. More people with an affinity for suburban living tend to reside in outerring, low-density suburbs instead of innerring, high-density suburbs. Thus, personality factors improve the understanding of transport behavior. On the other hand, personality characteristics cannot be affected by policy measures, while values for personality variables are hard to gather and predict. The problem of application of models with personality variables may be solvable for current (i.e. nonforecasting) applications if people can be classified into a small number of personality classes which can be assessed by a manageable number of attitudinal statements. As this study demonstrates, this is feasible.