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The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making.

This study aims to rank high crash risk areas and measure the impacts of accessibility and other spatial and socio-economic factors on county-level crash severities while controlling for spatial and temporal unobserved effects. Among other insights, the results suggest that primary roadway density is positively associated with all crash severities. Contrary, intersection density is negatively associated with injury and no injury crashes. Furthermore, school density and rates of driving under the influence are both positively associated with no injury crashes. Additionally, the mean travel time to the hospital is positively associated, while urbanity is negatively associated with fatal crashes. Unobserved effects due to unstructured random effect terms are significant and contribute to the model’s overall goodness of fit. The within-area correlation among crash severities is positive and non-negligible. From a temporal perspective, both fatal and no injury crashes have decreasing trends. However, no clear pattern for injury crashes is observed across the years. From a spatial perspective, extreme high and low crash risk counties are identified throughout the study area with no clear observed patterns of clustering or dispersion.

The recent development of Automated Traffic Signal Performance Measures (ATSPMs), has provided new opportunities and insights into traffic signal operations. As agencies begin to make decisions regarding investment in infrastructure and operation systems, it is imperative to understand the impacts these systems may have on safety. Past research has thoroughly investigated the impact of geometry and signal timing parameters on the safety of intersections, but little is understood on the relationship between improved signal performance and safety. This study uses vehicle trajectory data to create performance metrics for 121 signalized intersections on ten corridors near Columbus, Ohio. These metrics are used to understand the relationship between signal performance and safety. Two performance metrics, percent arrivals on green (POG) and level of travel time reliability (LOTTR), were used along with other volume and geometric data to model the total crash frequency on signalized mainline approaches. The crash data were modeled using a random parameters negative binomial approach. In consideration of potential unobserved heterogeneity between intersections, a correlated random parameters specification was tested alongside the traditional uncorrelated random parameters and fixed parameters model. Based on goodness of fit measures, the correlated random parameter model was chosen to interpret results because this model explains the complex cross-correlation among the estimates of random parameters. The elasticity values revealed a one percent increase in percent arrivals on green is associated with a reduction in total crashes by 1.12 %. The results of this study show the investment in signal operations and optimization result in an improvement in safety at signalized intersections. Further research should be explored to expand this study to additional intersections over a larger time period.

Intersection crashes are a safety concern for many transportation agencies, and crashes related to red light running (RLR) vehicles are of particular interest. Many camera-based RLR detection systems are controversial with the public, and there is relatively little published literature on the methodologies. This study proposes a methodology that combines high-resolution signal controller data with conventional stop bar loop detection to identify vehicles that enter the intersection after the start of red, when many of the most serious RLR crashes occur. The methodology was validated with on-site video collection at several locations, and the algorithm was refined to reduce the incidence of false RLR indications. One case study demonstrated that an increase on the side street of the green split from 20% to 24% of the cycle length was associated with a 34% reduction in daily RLR counts and a reduction in the likelihood of RLR by a factor of 1.7—a substantial safety improvement for minimal cost. Law enforcement and transportation agencies can use this technique to more efficiently manage and deploy safety resources, especially in cases for which detailed crash histories are unknown or infrequent.

In updating road-user fees, highway agencies are motivated by the twin objectives of equity and efficiency. The concepts of marginal pavement consumption cost (MPCC) and the fourth-power law have been used in past research in a bid to establish efficient and equitable user fees. Over the past few decades, the use of the fourth-power law has faced some criticism that the actual power of the relationship between loading and pavement deterioration deviates significantly from 4.0. In addressing this issue, this article presents a methodology that investigates the consequences of the power variations to the estimated MPCC and the distributional (equity) impacts of road-user fees for overweight (OW) trucks. The methodology incorporates practical schedules of highway maintenance, rehabilitation, and reconstruction. The analysis suggests that there are extremely small differences between the MPCC values that result from using different values of the exponent in the power law. However, different values of ...

The average delay is a commonly used measure for characterizing the performance of signalized intersections. Current methods for estimating the average delay rely on models that are based on traffic volumes and green times. In practice, the development of such real-time measurements of delay is challenging because of the difficulty of accurately measuring vehicle arrivals and departures. However, measuring the waiting time after the first vehicle's arrival during the red interval can be an important performance measure for low- and moderate-volume conditions. The maximum waiting time provides an upper bound, or maximum, on individual vehicle delay during a given cycle and facilitates comparisons between different types of operation. This paper demonstrates the effectiveness of this performance measure—maximum vehicle delay—with four case studies.

Maintaining and optimizing signal timing contribute directly to an improved end-user driving experience. With recent developments in crowdsourced vehicle probe data, travel time improvements associated with signal retimings can be quantitatively assessed without costly infrastructure. This study considers a three-stage evaluation of signalized corridor performance—( a) using existing controller settings, ( b) after synchronization of controller clocks and time-of-day plans, and ( c) after offset optimization—for the weekday morning peak, midday, and evening peak periods. The percentage of vehicles arriving on green and the vehicle travel time distributions were evaluated for each of the tasks in each period. The travel time data were then used to quantify user benefits with the mean–variance method to determine the dollar savings for the tasks performed. Signal time-of-day plan maintenance and clock synchronization accounted for some of the travel time benefits, but the savings were less reliable than with progression optimization, which improved both travel times and reliability.

Construction education has long been challenged with integrating meaningful classroom education activities with field internships. This paper describes a case study in which educational material was developed with use of time-lapse photography collected by student-deployed cameras on a large, fast-moving construction project. Several distinct construction activities were identified for time-lapse documentation, and corresponding engineering material quantities were computed to show the progression of work. These quantitative estimates were combined with the time-lapse footage in a series of educational video modules, which will be disseminated and archived online for long-term access by students and the public. The video modules were designed to educate students on the various steps involved in a major roadway rehabilitation project and provide them with a more relatable context for the various bid item quantities that they would encounter in their work as student interns and professional engineers. These methods can also be scaled across a variety of educational settings for different types of horizontal and vertical construction projects and thus, result in a more targeted and effective student learning experience.

In a period of increasingly constrained budgets, traffic engineers must develop more cost-effective maintenance strategies for traffic signal systems. Existing literature suggests that many...

Agencies often find it difficult to justify investments in active traffic management. Historically, it has been a challenge to obtain data that would help make the case for those investments. Although new data sources have emerged recently, there remains very little documentation of the potential long-term benefits from signal retiming using associated performance measures. This paper presents a use case for an active traffic management strategy on a signalized corridor over a 5-year period, during which traffic volumes increased by approximately 36% and offset optimization was performed every 2 to 3 years. Despite the considerable volume growth, the number of vehicles arriving on green increased by more than 41%, and the percentage of vehicles arriving on green increased by 10%, a gain of 6 percentage points. Furthermore, drivers experienced an average of 5% reduction in travel time and travel time reliability costs after each optimization. The retiming resulted in a 5-year net present value of approximately $3.7 million and a benefit–cost ratio of 52. Agencies can use these strategies to quantitatively assess how traffic performance and signal timing degrade over time, in a manner similar to physical infrastructure assets. The results highlight the benefits and associated business case of adopting a long-term active traffic management strategy that is based on data-driven performance monitoring and decision making.

The evaluation of driver behavior in dilemma zones is of critical importance to traffic safety and intersection design, but has thus far received limited investigation. Internal and external factors that can influence this behavior include driver demographics, the use of technology in the vehicle, and even the type of dilemma zone—Type I (based on signal timing/intersection geometry) or Type II (based on driver indecision). This paper explores the effect of cell phone technology, calling behavior, and driver characteristics on drivers' decisions to stop or go through an intersection at the onset of a yellow light, along with their corresponding rate of acceleration. A discrete/continuous analysis was adopted that accounted for potential model heterogeneity that may exist across the different dilemma zone types and drivers by applying selectivity-bias corrections. The results suggested that several technology and call behavior combinations are relevant in determining driver behavior, along with gender and age. It was also found that significant differences exist in driver behavior amongst those who decide to stop and those who decide to go through the intersection in a Type II dilemma zone situation. The findings of this paper can support efforts on reducing risky driving behavior in dilemma zones and enforcing distracted driving laws.

Serving protected left-turn phases for one or two vehicles can often be an inefficient use of cycle green time when the opposing through movements are over capacity. This paper assesses the performance of an intersection at which controller logic is applied to delay the call for a protected left-turn phase on the basis of vehicle wait times. During four weeks of evaluation, the delay on left-turn phase calls was varied in 25-s increments from 0 to 75 s. The results indicate that delaying left-turn phase initiation substantially increases the amount of green time for saturated through movements while minimally increasing the travel delay for left-turning drivers. The recommendation is made for agencies to consider using a delay in the range of 25 to 50 s for calling protected phases at intersections at which the opposing through movement is oversaturated and could benefit from additional green time. This paper presents one of the first quantitative studies evaluating potential intersection capacity and performance improvements with respect to left-turn detector delay.

This report discusses uses of and requirements for performance measures in traffic signal systems facilitated by high-resolution controller event data. Uses of external travel time measurements are also discussed. The discussion is led by a high-level synthesis of the systems engineering concepts for traffic signal control, considering technical and non-technical aspects of the problem. This is followed by a presentation of the requirements for implementing data collection and processing of the data into signal performance measures. The remaining portion of the report uses an example-oriented approach to showing a variety of uses of performance measures for communication and detector system health, quality of local control (including capacity allocation, safety, pedestrian performance, preemption, and advanced control analysis), and quality of progression (including evaluation and optimization).

Deployments of diverging diamond interchange (DDI) have increased in recent years. Most research has focused much effort on optimizing signal timing within the DDI, but there remains a need to optimize a DDI within an existing system to ensure smooth corridor operation. This paper presents a methodology for optimizing offsets on a corridor including a single-controller DDI. This methodology uses high-resolution controller data and an enhancement to the linkpivot algorithm that deconstructs the single-controller parameters into equivalent offset adjustments. The methodology is demonstrated by its application to a 5-intersection arterial route including a DDI, and the outcomes are assessed by measurement of travel times by Bluetooth vehicle re-identification. A user benefit methodology is applied to the travel time data that considers the reliability of the travel times in addition to the central tendency. Further, the methodology is applied to origin-destination (O-D) paths that travel to and from the freeway in addition to routes along the arterial. A total annualized user benefit of approximately $564,000 was achieved. The paper concludes by discussing how the method can also be applied to other nontraditional control schemes connected to arterials, such as continuous-flow intersections and TTI four-phase diamonds.

Average delay is perhaps the most commonly used measure for characterizing the performance of signalized intersections. Current methodologies for estimating the average delay rely on the use of models based on volumes and green times. In practice, it is challenging to develop such real-time measurements of delay, due to the difficulty of accurately measuring vehicle arrivals and departures. However, measuring wait time after the first vehicle arrival during the red interval can be an important performance measure for low and moderate volume conditions. The maximum wait time performance measure provides an upper bound, or maximum, on individual vehicle delay during a given cycle and facilitates comparison between different types of operation.

This study compares the performance of three signal re-timing scenarios – 1) premaintenance, 2) posttime-of-day and clock maintenance, and 3) post-progression optimization – for the weekday AM peak, mid-day, and PM peak time periods. The percentage of vehicles arriving on green and vehicle travel time distributions were evaluated for each of the tasks in each period. User benefits were then quantified using the travel time data with the mean-variance method to determine the dollar savings for the tasks performed. Signal time-of-day plan maintenance and clock synchronization accounted for some of the travel time benefits, but the savings were less reliable than progression optimization, which improved both travel times and reliability. Quantifying Benefits of Signal Timing Maintenance and Optimization Using both Travel Time and Travel Time Reliability Measures

Cycle length selection in corridor timing is often dictated by critical intersections with the highest level of saturation. Along corridors with balanced volumes and favorable link distances, a resonant cycle length is often sought to provide good progression in both directions of travel. This paper discusses the search for a resonant cycle length at a 5-intersection corridor in Fishers, Indiana over a three month period. The software traffic model suggests a reasonable range of cycle lengths from 104 seconds to 124 seconds for the corridor. This cycle length range is consistent with analytical highway capacity manual delay minimization approaches. A set of eleven cycle lengths from the 104 to 124-second range are tested over 12 weeks, with each iteration using optimized offset values generated by the Link Pivot progression optimization algorithm to maximize the percentage of vehicles arriving on green, and holding all phase splits constant. There was no obvious resonant cycle identified in the cycle length sweep, however the experiment findings indicate vehicles arriving on green decrease and travel times increase as cycle length increases.As a tradeoff, the number of force off phase terminations on the side-street phases decrease as a result of longer cycles indicating a better accommodation of sidestreet demand. Finally, a Seemingly Unrelated Regression (SURE) model was used to analyze the correlation between cycle lengths, percent of vehicles arriving on green, and travel time indicating a negative correlation between higher cycle length and progression performance.

This study estimated highway pavement and bridge damage costs, and analyzed the adequacy of permit revenues to cover these costs. The study began with an extensive review of the literature on the subject, thus facilitating identification of the gaps in the existing practice and research. The developed framework includes the establishment of asset families, realistic types and timings of reconstruction, rehabilitation, and maintenance, traffic volumes and growth projections. The cost of damage was estimated for each asset family and age group, and the sensitivity of asset damage cost with respect to key policy and analysis variables was explored. Finally, the study examined cost and operational issues associated with the enforcement of overweight truck policies. In addressing the gaps in existing research, this study showed that the damage cost of highway assets due to overweight trucks is influenced significantly by the asset type and age. For pavement assets, the pavement damage cost estimates were found to range from $0.006 per Equivalent Single Axle Load (ESAL)‐mile on Interstates to $0.218 per ESAL‐mile on non‐national highways. The study also showed that non‐consideration of reconstruction or maintenance cost can result in underestimation of the actual pavement damage cost by 79% and 83%, respectively. The analysis also showed that the unrealistic approach of considering only rehabilitation treatments applied at fixed intervals can lead to as much as 86% underestimation of the actual pavement damage cost. The results also suggest that pavement damage cost is highly sensitive to the pavement life‐cycle length, interest rate, rest period, and the costs and service lives of rehabilitation treatments. For the bridge assets (classified on the basis of their superstructure material type), the incremental methodology was found to be suitable to estimate the cost of bridge damage due to overweight vehicles. This methodology determines and assigns bridge damage cost to all vehicle classes on the basis of vehicle axle configurations and usage frequency (vehicle‐miles travelled). Incremental designs were carried out and cost functions were developed using Association of State Highway and Transportation Officials’ (AASHTO) design vehicles. Each Federal Highway Administration (FHWA) vehicle weight group was classified into an equivalent AASHTO loading regime using the modified equivalent vehicle (MEV) model which is based on gross vehicle weight, axle loading and axle spacing. The results were used to incrementally assign bridge cost to each vehicle class. For each class of overweight vehicles, the bridge damage cost was computed as the ratio of the cost responsibility and the volume of these vehicles. The bridge damage cost was estimated under two permit fee options and three user charging scenarios. The results suggest that approximately 22% of total bridge cost can be considered attributable to overweight vehicles. Also, it was observed that the bridge damage cost is not just a factor of gross vehicle weight but a function of all the three vehicle‐related variables: gross vehicle weight, axle spacing and axle loads. It was concluded that the adoption of a permit structure on the basis of gross vehicle weight only, will result in certain vehicle classes underpaying by as much as 92% of their actual contribution to bridge damage. Finally, the study examined cost and operational issues associated with the enforcement of overweight truck policies and identified a number of locations that could be considered for establishing new weigh stations and other enforcement facilities.

The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases for any developed nation today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting and converting energy for electric and transportation systems are numerous. Addressing costs, sustainability and resilience of electric and transportation needs requires long-term assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. Yet, the advent of electrically driven transportation, including cars, trucks and trains, creates potential interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modelling capability to perform long-term electric and transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow modelling with a multi-objective solution method. We describe and compare it to the state of the art in energy planning models. An example is presented to illustrate important features of this new approach.