Mobility Dynamics amid COVID-19 with a Case Study in Tennessee.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

Modeling spatiotemporal heterogeneity in interval-censored traffic incident time to normal flow by leveraging crowdsourced data: A geographically and temporally weighted proportional hazard analysis.

Non-recurrent traffic congestion arising from traffic incidents is unpredictable but should be addressed efficiently to mitigate its adverse impacts on safety and travel time reliability. Numerous studies have been conducted about incident clearance time, while the recovery time, due to the limitations of data collection, is often inadvertently neglected in assessing incident-induced duration (i.e., the time from incident occurrence to the normal flow of traffic). Overlooking the recovery time is likely to underestimate the total incident-induced impact. Furthermore, the spatiotemporal heterogeneity of observed factors is not adequately captured in incident duration models. To address these gaps, this study specifically investigated traffic crashes as they reflect safety issues and are the primary cause of non-recurrent congestion. The emerging crowdsourced traffic reports were harnessed to estimate crash recovery time, which can complement the blind zone of fixed detectors. A geographically and temporally weighted proportional hazard (GWTPH) model was developed to untangle factors associated with the interval-censored crash duration. The results show that the GWTPH model outperforms the global model in goodness-of-fit. Many factors present a spatiotemporally heterogeneous effect. For example, the global model merely revealed that deploying dynamic message signs (DMS) shortened the crash time to normal. Notably, the GWTPH model highlights an average reduction of 32.8% with a standard deviation of 31% in time to normal. The study's findings and application of new spatiotemporal techniques are valuable for practitioners to localize strategies for incident management. For instance, deploying DMS can be very helpful in corridors when incidents happen, especially during peak hours.

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.

Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth of vehicular motion data, which enables a better connection between crash frequency and driving behaviors. However, appropriately dealing with the spatial dependence of crash frequency and multitudinous driving features has been a difficult but critical challenge in the prediction process. To this end, this study aims to investigate a new Artificial Intelligence technique called Geographical Random Forest (GRF) that can address spatial heterogeneity and retain all potential predictors. By harnessing more than 2.2 billion high-resolution connected vehicle Basic Safety Message (BSM) observations from the Safety Pilot Model Deployment in Ann Arbor, MI, 30 indicators of driving volatility are extracted, including speed, longitudinal and lateral acceleration, and yaw rate. The developed GRF was implemented to predict rear-end crash frequency at intersections. The results show that: 1) rear-end crashes are more likely to happen at intersections connecting minor roads compared to major roads; 2) a higher number of hard acceleration and deceleration events beyond two standard deviations in the longitudinal direction is a leading indicator of rear-end crashes; 3) the optimal GRF significantly outperforms Global Random Forest, with a 9% lower test error and a substantially better fit; and 4) geographical visualization of variable importance highlights the presence of spatial non-stationarity. The proposed framework can proactively identify at-risk intersections and alert drivers when leading indicators of driving volatility tend to worsen.

A Kriging based spatiotemporal approach for traffic volume data imputation.

Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods.

Driver behaviours on rural highways with and without curbs - a driving simulator based study.

The speed limit of 55 mph (88 km/h) is used typically on rural highways in the U.S. When curbs are installed, a lower speed limit is suggested because running into curbs at high speeds may cause significant vehicular damage and severe injuries. However, it has been argued that lowering the speed limit may cause confusion in drivers, who do not perceive the risk and tend to operate their vehicles at the same speed as before. To better understand driver behaviour on two-lane rural highways before and after curb installation, the authors conducted a series of experiments on a high-fidelity driving simulator in different posted speed limit, curb installation, lateral curb clearance, weather, visibility, and traffic conditions. Results of the study suggest that driver behaviours are influenced by the various factors in a complex and interrelated manner. It is likely that curbs have no influence on a driver's selection of speed. Drivers do perceive the risk from the curb or the opposing traffic when selecting their lane positions. The available space between the curb and the opposing traffic is crucial and has significant effects on driving behaviours. The subjective effects of drivers are found to be influential to driving behaviours.

The influence of curbs on driver behaviors in four-lane rural highways--A driving simulator based study.

The speed limit of 55mph (88km/h) is typically used on rural highways in the U.S. For locations where curbs are installed along these roadways, some transportation agencies have suggested the use of a lower 45mph (72km/h) speed limit because, according to AASHTO, running into curbs at high speeds may cause significant vehicular damage and even severe injuries. However, it has also been argued that lowering the speed limit after the installation of curbs may cause confusion in drivers, who do not perceive the risk associated with the newly installed curbs and tend to operate their vehicles at the same speed as before. To better understand driver behavior on rural highways before and after curb installation, and with different speed limits, researchers at the University of Tennessee conducted a series of experiments in two-lane and four-lane highways on a high-fidelity driving simulator. This paper mainly presents the findings from the four-lane study, and compares the results from the previous two-lane study. The scenario matrix consists of several dimensions including posted speed limit (45 and 55mph, or 72 and 88km/h), curb installation, lateral clearance between the edge of travel lane and the curb (2ft, 6ft, and no-curb, or 0.6m, 1.8m, and no-curb), weather (clear and fog), traffic conditions in the next lane (1400veh/h and 400veh/h), etc. For each subject under different experimental scenarios, detailed driving parameters, such as driving speed and vehicle position in the travel lane, were recorded and analyzed subsequently. Results of the study suggest that driver behaviors are influenced by the various factors in a complex and interrelated manner. It is likely that drivers do not perceive the risk from the curb in determining their speed on four-lane rural highways. However, it is found that curbs may provide certain guidance to drivers, especially in selecting lane position. Compared to the previous research in two-lane conditions, it is found that drivers are more likely to choose driving speeds according to posted speed limits, rather than lane configurations. It is also found that the relative speed between driver's vehicle and ambient traffic or curbs is an important factor determining drivers' perception of risk and thus their driving behavior. The influence of subjective effects of these factors to their driving behavior is also observed in the study.

Effects of countdown timers on driver behavior after the yellow onset at Chinese intersections.

OBJECTIVES: Few studies have focused on the effect of countdown timers at signalized intersections in China, where such timers are widely deployed for their perceived benefits of increased safety and capacity. This study examines the effect of countdown timers on driver behavior during the yellow interval. METHOD: Signal phasing and traffic operations were videotaped at 4 comparable signalized intersections under normal conditions. Microscopic details were extracted manually at 25 Hz to yield 24 h of data on onset time of the yellow, onset time of the red, driver location and actions after the onset of the yellow, red light-running violations, etc. For comparable intersections with and without countdown timers, driver behavior measured by driver decision (stop or go) and vehicle entry time (when the vehicle crosses the stop line) were analyzed using binary logistical regression (BLR) and a nonparametric test, respectively. RESULTS: The results suggest that countdown timers can indeed influence driver behaviors, in terms of decisions to stop or cross the intersection as well as the distribution of vehicle entry times. There was a strong correlation between the presence of countdown timers and an increase in red light violations. CONCLUSION: Countdown timers may lead to increased entrance into the intersection during the later portions of the yellow and even the red. This alarming finding calls for further research as well as for serious consideration before the field deployment of countdown timers.

Train-vehicle crash risk comparison between before and after stop signs installed at highway-rail grade crossings.

OBJECTIVE: The safety benefit of stop sign treatment employed at passive highway-rail crossings has been a subject of research for many years. The objectives of this research is to investigate whether and to what degree the crash rate has changed at previously passive grade crossings after stop signs were implemented and examine whether and how the crash characteristics (associated with vehicle type, crossing surrounding, crossing design, crash severity, etc.) changed subsequently. METHODS: Federal Railroad Administration grade crossing databases during the 26-year period (1980-2005) were applied in this study. Among the stop-controlled grade crossings, a total of 7394 "target" crossings were identified to be once crossbucks controlled and subsequently upgraded with the installation of stop signs without the implementation of other traffic control devices during the study period. Each target crossing was further divided into two time periods: when it was controlled by crossbucks only (before) and when it was controlled by stop signs (after). Both annual crash rate analysis and crash propensity analysis of before-after stop sign installation are conducted to quantify the safety benefit of stop sign treatment. RESULTS: It was found that during the 26-year period (1980-2005), the annual crash rates when the crossings were controlled by crossbucks-only were consistently higher than the crash rates when the crossings were controlled by stop signs. The further crash propensity analysis indicated that the stop sign treatment was especially effective at crossings with higher annual average daily traffic (AADT), advanced warning signs, sight distance problem, adverse lighting conditions; the motorist-stopped-on-crossing, did-not-stop, and injury crash risks were also significantly reduced after stop signs were applied. CONCLUSIONS: The finding of this study suggested that the vehicle volume should be included into the guideline for stop sign use. Therefore, engineers and decision makers are encouraged to routinely check available sight distances at passive crossings controlled by crossbucks only and add stop signs to the crossings with insufficient sight distances. Additionally, it is suggested that advanced warning signs should be jointly used at stop-controlled crossings to maximize the safety effect. However, stop signs were less effective at crossings with higher train speeds or track classifications, where active warning devices may be a better safety solution for grade crossings.