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Real-time traffic information such as traffic speed is essential to a variety of practical applications. Because of the high installation and maintenance cost of convention data collecting methods, transportation engineers have sought alternative data sources to monitor traffic. Probe-based traffic data, such as Waze traffic data, could serve as alternative data sources in traffic management, but this source has not been thoroughly explored nor evaluated. Using the 10.8 mile stretch of I-40 in Knoxville, Tennessee, we compared the speed measurements from both Waze and Remote Traffic Microwave Sensors (RTMS) over two months and explored the characteristics of Waze traffic speed data. These are the main findings: 1) These two datasets showed a similar pattern with slight differences. Waze speeds tend to be higher than RTMS speeds for high speed, while Waze speeds are more likely to be similar or even lower than RTMS speeds for low speed; 2) several factors affecting the speed differences between RTMS speeds and Waze speeds were identified, such as Waze speed value, time of day (peak hour vs. non-peak hour), AADT (Annual Average Daily Traffic), and segment length; and 3) Waze reported the same speed for several successive reporting periods if the real-time speed is not available. The effective Waze sampling period is about two to four minutes. Waze speeds had more real-time speed observations during congested times, indicating that Waze speeds are more reliable for congested scenarios. The findings may lead to a better understanding of this source of data and any resulting analysis. [ABSTRACT FROM AUTHOR]

Highlights • Over the past decades, rapid and continued population growth and urban expansion into fringe areas makes previously remote and obscure hazardous installations potential sites for major urban disasters. • Building on the past hazard-specific risk assessment attempts, this study developed a comprehensive multi-hazard spatial risk assessment framework for multiple sites and types of hazardous installations in metropolitan areas. • The framework consists of three GIS-based quantitative models for the analysis of the intrinsic risks associated with, the accessibility for rescue operations in the proximity of, and the potential effectiveness of mass evacuation from these hazard installations. Abstract With rapid economic growth, previously less populated peripheral areas of major cities in China have now become densely populated. Many hazardous installations (e.g., chemical plants, tank farms, depots of explosive materials, etc.) that used to be in these less populated areas are now found in the backyards of millions of citizens going about their daily lives with minimal knowledge about the dangers these installations pose. A hazard mishap that may have been containable with no human loss just 30 years ago can now threaten the lives of millions. While numerous studies have assessed hazard-specific risks, few efforts have attempted to establish a comprehensive framework to assess multi-hazard scenarios. For this purpose, this study developed a framework for spatial risk assessment of multiple hazardous installations in a metropolitan area. The framework includes three quantitative models for the analysis of the inherent danger associated with, the accessibility for rescue operations at, and the potential effectiveness of mass evacuation from these hazardous installations. Based on this framework and its quantitative models, a case study of the city of Beijing was conducted in this research. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) usually require monitoring of massive road networks and gathering traffic data at a high spatial and temporal resolution. This leads to the accumulation of substantial data volumes, necessitating the development of more concise data representations. Approaches like principal component analysis (PCA), which operate within subspaces, can construct precise low-dimensional models. However, interpreting these models can be challenging, primarily because the principal components often encompass a multitude of links within the traffic network. To overcome this issue, this study presents a novel approach for representing and indexing network traffic conditions through weighted CUR matrix decomposition integrated with clustering analysis. The proposed approach selects a subset group of detectors from the original network to represent and index traffic condition through a matrix decomposition method, allowing for more efficient management and analysis. The proposed method is evaluated using traffic detector data from the city of Nashville, TN. The results demonstrate that the approach is effective in representing and indexing network traffic conditions, with high accuracy and efficiency. Overall, this study contributes to the field of network traffic monitoring by proposing a novel approach for representing massive traffic networks and exploring the effects of incorporating clustering into CUR decomposition. The proposed approach can help traffic analysts and practitioners to more efficiently manage and analyze traffic conditions, ultimately leading to more effective transportation systems. [ABSTRACT FROM AUTHOR]

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The "median family income," "timberland annual growth-to-removal ratio," and "transportation delays" were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site locations, and locations not preferential for a mill location. The model predicted that higher probability locations for smaller biomass mills (feedstock capacity, the size of sawmills) were in southern Alabama, southern Georgia, southeast Mississippi, southern Virginia, western Louisiana, western Arkansas, and eastern Texas. The higher probability locations for large capacity mills (feedstock capacity, the size for pulp and paper mills) were in southeastern Alabama, southern Georgia, central North Carolina, and the Mississippi Delta regions. [ABSTRACT FROM AUTHOR]

Travelers' route choice behaviors affect the distribution of network flow. The paper presents an unconventional equilibrium flow model that extends the well-known non-Walrasian equilibrium theory of economics to the analysis of route choice behavior. With the introduction of path residual capacity as the quantity signal, a quantity adjustment user equilibrium flow model depicts the route choice dynamics based on the maximum path residual capacity, and thus differs from the traditional traffic equilibrium pattern regulated by path travel time. An equivalent nonlinear complementary problem to the proposed model is developed, and further reformulated as an unconstrained mathematical program using a gap function, which permits many efficient solution algorithms. The maximum residual capacity path algorithm and the maximum residual capacity traffic assignment algorithm are developed, and the gradient descent algorithm is used to solve the unconstrained mathematical program. Numerical results show that the proposed model and solution algorithm are feasible and effective. The paper proposes that both price adjustment and quantity adjustment are special cases of the price-quantity adjustment principle, and the equilibrium flow model based on which can capture route choice behaviors that has not been modeled in previous studies. • A quantity adjustment user equilibrium flow model illustrates route choice dynamics. • Extends the non-Walrasian equilibrium theory to route choice behavior analysis. • The path residual capacity serves as the quantity signal. • An equivalent nonlinear complementary problem is used to solve the proposed model. [ABSTRACT FROM AUTHOR]

Abstract: Automated enforcement red-light cameras (RLC) have been widely adopted by municipalities around the world as a measure of curbing red-light running (RLR) at signalized intersections and reducing the cost of law enforcement. While a consensus has not yet been reached about whether RLC in general can benefit intersection safety by reducing RLR and crashes, recent debates revolve around using RLC as a revenue generator. Some of the political backlash of RLC is the perception that they are installed primarily to fulfill revenue guarantees and sustain the RLC program. Some municipalities have been charged with changing the signal phasing to trap more red-light runners and increase the revenue from RLC programs. This paper focuses on a number of engineering strategies, mainly related to signal timing that may be used by municipalities to achieve their financial goals. The negative impacts of implementing these measures on the safety and efficiency of intersection operations and public support on RLC programs are also discussed. These strategies are also revealed to increase transparency of the divergent motivations of RLC vendors, municipalities, policy makers and safety advocates. [Copyright &y& Elsevier]

Abstract: Weigh stations are the primary weight compliance checkpoints for commercial trucks. In the past decades, states have deployed weigh-in-motion (WIM) technology to reduce delay and increase weight violation enforcement. However, due to limitations on the accuracy and implementation of WIM technology the full potential of WIM has not been realized. To this end, this paper presents an adaptive algorithm that improves the efficiency of WIM-equipped weigh stations based on real-time weigh station queue length, truck demand, weight distribution, and static scale service time. Instead of using one fixed weight threshold value, our proposed floating-threshold control algorithm increases the threshold value based on the queue length at the weigh station to avoid station closure while still identifying the worst weight offenders: the threshold value is lowered when the queue is short to maximize the number of inspection. We develop the queueing models for both the fixed-threshold algorithm and the floating threshold algorithm and derive their mathematical formula to calculate the measures of effectiveness of a weigh system. A simulation model is developed to compare the performance of the proposed adaptive algorithm with a traditional fixed-threshold algorithm under various situations. The simulation results show that an adaptive algorithm yields higher violator capture percentage, lower station closure frequency, and less delay to non-violating trucks. [Copyright &y& Elsevier]

Abstract: License-plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring, and access control. Recently, there has been effort to exploit an LPR database for vehicle tracking using popular template matching procedures. Existing template matching procedures assume that the true reference string is always available. However, under a two-point LPR survey, a vehicle could have its plate misread at both locations generating a pair of misread strings (or templates) with no reference for matching. To compensate for LPR misreading problem, we propose a new weight function based on a probability model to match the observed outcomes of a dual LPR setup. Also, considering that reversal errors are never made in LPR machines, new editing constraints as a function of the string lengths are proposed to avoid compensation for reversal errors. These editing constraints are incorporated into the constraint edit distance formulation to improve the performance of the matching procedure. Finally, considering that previous template matching procedures do not take advantage of passage time information available in LPR databases, we present an online tracking procedure that considers the properties of probability distribution of vehicle journey times in order to increase the probability of correct matches. Experimental results show that our proposed procedure can improve the accuracy of LPR systems and achieve up to 97% of positive matches with no false matches. Further research is needed to extend the ideas proposed herein to plate-matching with multiple, i.e., more than two, LPR units. [Copyright &y& Elsevier]

This paper presents a method of addressing stochastic variation at closely spaced signalized intersections to provide secondary coordination to 'minor' movements with significant traffic volumes. A neurofuzzy signal control system was designed in this study to manage a noncoordinated movement to avoid queue spillback. Building on the conventional actuated-coordinated control system, the neurofuzzy controller does not lose the benefit of the primary coordination of the conventional controller but establishes a 'secondary coordination' between the upstream coordinated phase (through phase) and the downstream noncoordinated phase (left-turn phase) on the basis of a real-time traffic demand. Under the neurofuzzy signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left-turn lane and be served in a timely fashion and thus reduce the likelihood of being delayed at the downstream intersection. The simulation results indicate that the neurofuzzy signal control consistently outperformed the conventional actuated-coordinated controller in terms of reduction in systemwide average delay and number of stops per vehicle under a wide range of traffic volumes by nearly 20% under heavier demand conditions. [ABSTRACT FROM AUTHOR]

This paper demonstrates that in addition to intracycle demand fluctuation, which is already a consideration in many delay models, intercycle demand variance also impacts average delay at signalized intersections. Webster-type delay models treat demand fluctuation over the whole analysis period, often 15 min or longer, as if it were just within a single cycle. Such an approach is fine if used judiciously, one might presume. However, results from Monte Carlo simulations with the incremental queue accumulation (IQA) method indicate that Webster-type delay models will underestimate the average delay under heavy traffic conditions. As unutilized capacity at a signalized intersection cannot be saved or carried over to be used by succeeding cycles when demand surges due to normal fluctuation, better understanding of the patterns of intercycle demand variance is important. Simulation results demonstrate that different patterns of intercycle demand variance can result in different levels of average delay. A low-to-high demand pattern will cause a higher average delay than a high-to-low pattern would, even though the overall demand level is exactly the same. It is therefore clear that neglecting intercycle demand variance may lead to significant inaccuracy and, hence, suboptimal signal timing decisions. [ABSTRACT FROM AUTHOR]

Evacuation plans are traditionally based on empirical knowledge and local experience. Many studies are underway to improve and, even, optimize the process of evacuation planning and operations. To this end, a commonly accepted set of well-defined measures of effectiveness (MOEs) for evacuation is necessary. To guide various optimization processes and assess their benefits, this paper examines MOEs in literature, whether implicitly assumed or explicitly defined, and subsequently proposes a comprehensive four-tier MOE framework, taking into consideration of evacuation time, cumulative exposure, and temporal/spatial risk factors. For each tier of MOE, the optimization formulation is presented. [ABSTRACT FROM AUTHOR]

Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

• Crowdsourcing data offers a new solution to estimate incident recovery time. • A spatiotemporally weighted hazard model is developed to model the interval-censored incident time to normal traffic flow. • The proposed model produces higher prediction accuracy than the global model by capturing unobserved spatiotemporal heterogeneity. • The proposed model uncovers the spatiotemporal heterogeneous effects of multiple factors. 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. [ABSTRACT FROM AUTHOR]

• A data-adaptive spatio-temporal traffic queue detection algorithm is proposed. • The algorithm builds on traffic flow fundamentals combined with a Gaussian mixture model. • The proposed algorithm is capable of accounting for varying queueing conditions. • Different queue types were successfully detected by the algorithm. • The proposed algorithm outperformed speed threshold methods using detector data. When traffic flow exceeds capacity because of demand fluctuations, crashes, work zones, and special events, a traffic queue is formed on a highway. Traffic queues cause potentially hazardous situations at the end of the queue where drivers unexpectedly face slowed or stopped traffic while approaching at high speed. Therefore, detecting a queue is vital for protecting it. This study presents a real-time spatio-temporal traffic queue detection algorithm that builds on traffic flow fundamentals combined with a statistical pattern recognition procedure. Using flow-density data, traffic flow phase is classified as either congested or uncongested flow in a probabilistic manner, based on Gaussian mixture models for each location in such a way that detects the traffic phase transitions. The proposed detection algorithm was applied to detect traffic queues using traffic detector data from Interstate 40 in Knoxville, Tennessee. The detection results show that the algorithm detects queues successfully by accounting for varying queueing conditions and different queue types. In addition, the proposed algorithm performed better than speed threshold methods in the literature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Spatiotemporal evaluation of micromobility data can support data-driven decisions. • Unsupervised machine learning identified five distinct e-scooter usage patterns. • The most popular type (29%) of e-scooter was daytime short errand trips. • Only 16% of trips were nighttime entertainment district trips. • E-scooter ridership increases during weekends and summer months in general. Electric scooters (e-scooters) are becoming one of the most popular micromobility options in the United States. Although there is some evidence of increased mobility, reduced carbon emissions, replaced car trips, and associated public health benefits, there is little known about the patterns of e-scooter use. This study proposes a framework for high-resolution analysis of micromobility data based on temporal, spatial, and weather attributes. As a case study, we scrutinized more than one million e-scooter trips of Nashville, Tennessee, from September 1, 2018, to August 31, 2019. Weather data and land use data from the Nashville Travel Demand Model and scraping of Google Maps Point of Interest (POI) data complemented the trip data. The combination of Principal Component Analysis (PCA) and a K-means unsupervised machine learning algorithm identified five distinct e-scooter usage patterns, namely morning work/school, daytime short errand, social, nighttime entertainment district, and utilitarian trips. Among other findings, the most common use of e-scooters in Nashville was daytime short errand trips, contributing to 29% of all e-scooter trips. We found that 7% of all e-scooter trips resembled morning commuting to work or school. Only 16% of trips were classified as Nighttime Entertainment District trips. The average daily number of trips on a typical weekend was 81% higher than a typical weekday. We also found variation in e-scooter usage patterns over a year with high summer ridership patterns. The findings of this study can help city administrations, planners, and micromobility operators to understand when and where people are using e-scooters. Such knowledge can guide them in making data-driven decisions regarding safety, sustainability, and mode substitution of emerging micromobility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the impact of countdown timer on driving maneuvers after the onset of yellow at signalized intersections in China. Signal phasing and traffic operations were videotaped at four comparable intersections under normal conditions in Changsha city, China. Microscopic details were extracted to yield 24h worth of data including the onset time of yellow, the onset of red, vehicle location, and maneuvers after the onset of yellow. The difference in driver’s stop/go decisions at the countdown and non-countdown timer installed intersections were investigated with binary-logistical regression analysis. By classifying the driving population into several distinct groups, this study further investigates the correlation between the countdown timer and two hazardous maneuvers: aggressive acceleration and conservative (abrupt) stopping, respectively. The descriptive results, though preliminary, indicate that the countdown timer can better assist drivers in their decision-making process and thus may reduce hazardous driving maneuvers during the phase transition period. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

The US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models. [ABSTRACT FROM AUTHOR]

• This study is among the first to harness crowdsourced Waze reports for pavement pothole detection and evaluation. • STKDE highlights when and where potholes are likely to occur. • ST-DBSCAN identifies potential isolated potholes and pothole zones. • Waze pothole reports can contribute to earlier and precise detection of potholes and even extra potholes unnoticed by field crews. Identifying and fixing roadway potholes promptly can improve roadway safety and decrease vehicular damage, which is essential in transportation infrastructure management. Transportation agencies are constantly looking for efficient solutions for pavement intelligent management and maintenance. Traditionally, laser scanning, accelerometers, and videos are the major detecting sources employed to diagnose pavement distress. However, the responsiveness of these methods is constrained by the limited amount of equipment and labor. By contrast, popular crowdsourced tools like the navigation app Waze provide citizens with a cost-effective way to feed real-time roadway information, which could facilitate the evaluation, detection, and repair of potholes. To examine these possibilities, the study collected one-year pothole reports for five critical corridors in Nashville, Tennessee as a case study. Firstly, a Spatial Temporal Kernel Density Estimation (STKDE) was proposed to estimate the likelihood of potholes over space and time. The results highlight the spatiotemporal variation of pothole propensity. Furthermore, the hotspots recognized at a 95% confidence level revealed the significantly vulnerable areas and times, such as I-40 Westbound near log mile marker 13 from January to June 2021. Secondly, based on the optimal bandwidths generated by STKDE, spatiotemporal DBSCAN clustering was performed to identify potholes. Two pothole patterns are identified and interpreted as isolated spots and pothole zones, respectively. It was also found that crowdsourced reports come up much sooner than work requests in the Pavement Management System (PMS). Moreover, pothole reports also identified several blind spots ignored by the maintenance team. This study contributes to facilitating pavement management and maintenance with emerging crowdsourced data. [ABSTRACT FROM AUTHOR]

With the emergence of the internet of things, pathfinding problems have recently received a significant amount of attention. Various commercial applications provide automated routing by considering travel time, travel distance, fuel consumption, complexity of the road, etc. However, many of these prospective applications do not consider route safety. Emergence of high-resolution big data generated by connected vehicles (CV) helps us to integrate safety into routing problem. The goal of this study is to address safety aspects in pathfinding problems by developing a methodological framework that simultaneously considers safety and mobility. To reach this goal, the concept of volatility is utilized as a surrogate safety performance measure to quantify route safety and driver behavior. The proposed framework uses CV big data and real-time traffic data to calculate safety indices and travel times. Measured safety indices include 5-year crash history, route speed and acceleration volatility, and driver volatility. Travel time and safety shape a cost function called "route impedance." The algorithm has the flexibility for the user to predefine the weight for safety consideration. It also uses driver volatility to automatically increase safety weight for volatile drivers. To illustrate the algorithm, a numerical example is provided using an origin-destination pair in Ann Arbor, MI, and more than 42 million CV observations from around 2,500 CVs from the Safety Pilot Model Deployment (SPMD) were analyzed. The sensitivity analysis is performed to discuss the impact of penetration rate of CVs and time of the trip on the results. Finally, this paper shows suggested routes for multiple scenarios to demonstrate the outcome of the study. The results revealed that the algorithm might suggest different routes when considering safety indices and not just travel time. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) represent a new, growing segment of transportation research. While there have been prior studies and deployments of AVs worldwide, full autonomy in bus transit has gained interest among researchers and practitioners within the last decade, which presents an opportunity to synthesize early trends. Therefore, the objective of this paper is to provide a review of the latest research on fully autonomous buses to summarize findings and identify gaps needing future research. Forty studies were reviewed in detail, and five main themes were identified, which are (1) technology deployment; (2) user acceptance; (3) safety; (4) social and economic aspects; and (5) regulations, policies, and legal issues. The results reveal that most prior studies have focused on technology development, and the area of regulation and policy would benefit from additional study. Noteworthy differences between research in Europe and the United States were also identified. In Europe, large funded projects involving real-world deployments have focused on user acceptance, security and safety, costs, and related legal issues, whereas in the United States, research has primarily concentrated on simulation modelling with limited real-world deployments. The results of this review are important for policy-makers and researchers as AV technology continues to evolve and become more widely available. [ABSTRACT FROM AUTHOR]

Obtaining accurate speed and travel time information is a challenge for researchers, geographers, and transportation agencies. In the past, traffic data were usually acquired and disseminated by government agencies through fixed-location sensors. High costs, infrastructure demands, and low coverage levels of these sensor devices require agencies and researchers to look beyond the traditional approaches. With the emergence of smartphones and navigation apps, location-based and crowdsourced Big Data are receiving increased attention. In this regard, location-based big data (LocBigData) collected from probe vehicles and road users can be used to provide speed and travel time information in different locations. Examining the quality of crowdsourced data is essential for researchers and agencies before using them. This study assessed the quality of Waze speed data from surface streets and conducted a case study in Sevierville, Tennessee. Typically, examining the quality of these data in surface streets and arterials is more challenging than freeways data. This research used Bluetooth speed data as the ground truth, which is independent of Waze data. In this study, three steps of methodology were used. In the first step, Waze speed data was compared to Bluetooth data in terms of accuracy, mean difference, and distribution similarity. In the second step, a k-means algorithm was used to categorize Waze data quality, and a multinomial logistics regression model was performed to explore the significant factors that impact data quality. Finally, in the third step, machine learning techniques were conducted to predict the data quality in different conditions. The result of the comparison showed a similar pattern and a slight difference between datasets, which verified the quality of Waze speed data. The statistical model indicates that that Waze speed data are more accurate in peak hours than in night hours. Also, the traffic speed, traffic volume, and segment length have a significant association on the accuracy of Waze data on surface streets. Finally, the result of machine learning prediction showed that a KNN method performed the highest prediction accuracy of 84.5% and 82.9% of the time for training and test datasets, respectively. Overall, the study results suggest that Waze speed data is a promising data source for surface streets. • Location-based Crowdsource speed data provided by Waze were used to examine the quality of these data types. • Bluetooth speed data (an independent data source) was used as ground truth. • The study use data from surface streets and arterial which is more challenging than freeways. • The authors conduct a script to extract the real-time data and archive them. • Crowdsourced big data, such as Waze, is a viable alternative to fixed-location traditional methods, even on surface streets. [ABSTRACT FROM AUTHOR]

Modern transportation systems rely increasingly on the availability and accuracy of traffic detector data to monitor traffic operational conditions and assess system performance. Missing data, which occurs almost inevitably for a number of reasons, can lead to suboptimal operations and ineffective decisions if not remedied in a timely and systematic fashion through data imputation. A review of literature suggests that most traffic data imputation studies considered the temporal continuity of the data but often overlooked the spatial correlations that exist. Few of the studies explored the randomness of the patterns of the missing data. Therefore, this paper proposes two cokriging methods that exploit the existence of spatio-temporal dependency in traffic data and employ multiple data sources, each with independently missing data, to impute high-resolution traffic speed data under different data missing pattern scenarios. The two proposed cokriging methods, both using multiple independent data sources, were benchmarked against classic simple and ordinary kriging methods, which use only the primary data source. An array of testing scenarios were designed to test these methods under different missing rates (10–40% data loss) and different missing patterns (random in time and location, random only in location, and non-random blocks of missing data). The results suggest that using multiple data sources with the spatio-temporal simple cokriging method effectively improves the imputation accuracy if the missing data were clustered, or in blocks. On the other hand, if the missing data were randomly scattered in time and location, the classic ordinary or simple kriging method using only the primary data source can be more effective. Our study, which employs empirical traffic speed data from radar detectors and vehicle probes, demonstrates that the overall predictions of the kriging-based imputation approach are accurate and reliable for all combinations of missing patterns and missing rates investigated. [ABSTRACT FROM AUTHOR]

• Big data generated by connected vehicles is used for proactive safety management. • A new Geographical Random Forest model is developed to uncover spatial heterogeneity and dependency. • Local Random Forest appropriately handles multicollinearity. • Pre-crash driving volatility measures (e.g., strong lateral acceleration) are leading factors of crash frequency. • Variable Importance of predictors provides insights on potential countermeasures for at-risk intersections. 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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Multivariate state space models are developed for network traffic flow prediction. [•] Feeding data are from historical time series and neighboring detector measurements. [•] The proposed models are more accurate and robust. [•] The NSS model is a better alternative for non-congested flow rate prediction. [•] The CSS model is a better alternative for congested time mean speed prediction. [Copyright &y& Elsevier]

Highlights: [•] Define a space–time use efficiency metric based on trajectories. [•] Propose a two-tier hybrid multi-objective optimization algorithm. [•] Plan vehicle and pedestrian turning movement directions. [•] Assess space–time use efficiency of an evacuation plan. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a wavelet-based novel freeway automated incident detection algorithm with varying threshold parameters considering the level of traffic flow. In this approach, new test statistics for incident detection are extracted from occupancy and speed data using discrete wavelet transform, which decomposes traffic measurements into different resolution-time components. Unlike conventional incident detection algorithms, which apply fixed threshold values and often result in undesirably high false alarm rates, our proposed algorithm varies its threshold values adaptively based on the level of traffic volume. We have derived the mathematical relationship between the false alarm probability and the threshold value of our proposed decision function. For a given target false alarm rate, the threshold values can be changed adaptively depending on the traffic levels of normal traffic conditions. Also, we propose the new feature selection technique to measure the quality of different features that may be used to discriminate between normal and incident traffic conditions. Using both simulated data set and real-life incident data set, the performance of our proposed algorithm was compared with existing popular approaches such as California algorithm, Minnesota algorithm, conventional neural networks algorithm, and a wavelet-based neural-net algorithm. Experimental results show that the proposed wavelet-based algorithm consistently outperformed others with a higher detection rate, lower false alarm rate, and shorter mean time to detection. It is conclusive that the proposed algorithm is a superior alternative to existing algorithms. [ABSTRACT FROM AUTHOR]

The recent hazardous material (hazmat) shipment ban in Washington, D.C., has led to debates, legal challenges, and considerations by other major cities to pursue similar actions. This article presents a methodology for evaluating hazmat shipment routing options on railroad networks under situations such as the shipment ban. A case study involving three alternatives is presented. Population and other vulnerable people within a 0.8 km (or 0.5 mile) radius buffer zone along the rail line are used to evaluate the potential risk associated with ultra-hazardous material (i.e., explosives, flammable gasses, poisonous gasses, and poisonous materials) shipments. Based on this study, it is concluded that moderate increases in ton-km, and subsequently time in transit, will be a result from the rerouting. On the other hand, the overall population at risk will see a reduction. The population-at-risk burden, however, is simply shifted from one location to other locations. This article also identifies areas for potential follow-up efforts. [ABSTRACT FROM AUTHOR]

Abstract: Most literature on short-term traffic flow forecasting focused mainly on normal, or non-incident, conditions and, hence, limited their applicability when traffic flow forecasting is most needed, i.e., incident and atypical conditions. Accurate prediction of short-term traffic flow under atypical conditions, such as vehicular crashes, inclement weather, work zone, and holidays, is crucial to effective and proactive traffic management systems in the context of intelligent transportation systems (ITS) and, more specifically, dynamic traffic assignment (DTA). To this end, this paper presents an application of a supervised statistical learning technique called Online Support Vector machine for Regression, or OL-SVR, for the prediction of short-term freeway traffic flow under both typical and atypical conditions. The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models. The resultant performance comparisons suggest that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies. Yet OL-SVR is the best performer under non-recurring atypical traffic conditions. It appears that for deployed ITS systems that gear toward timely response to real-world atypical and incident situations, OL-SVR may be a better tool than GML. [Copyright &y& Elsevier]

Abstract: Traffic volume is a fundamental variable in several transportation engineering applications. For instance, in transportation planning, the annual average daily traffic (AADT) is a primary element that has to be estimated for the year of horizon of the analysis. The huge amounts of money to be invested in designed transportation systems are strongly associated with the traffic volumes expected in the system, which means that it is important that the AADT should be accurately predicted. In this paper, a modified version of a pattern recognition technique known as support vector machine for regression (SVR) to forecast AADT is presented. The proposed methodology computes the SVR prediction parameters based on the distribution of the training data. Therefore, the proposed method is called SVR with data-dependent parameters (SVR-DP). Using 20 years of AADT for both rural and urban roads in 25 counties in the state of Tennessee, the performance of the SVR-DP was compared with those of Holt exponential smoothing (Holt-ES) and of ordinary least-square linear regression (OLS-regression). SVR-DP performed better than both methods; although the Holt-ES also presented good results. [Copyright &y& Elsevier]