Local government authority attitudes to road traffic CO₂ emissions modelling: a British case study

Local government authorities (LGAs) play a key role in facilitating mitigation of road traffic CO2 emissions and must engage in emissions modelling to quantify the impact of transport interventions. Existing Emissions Model (EM) methodologies range from aggregate to disaggregate approaches, with more detail normally entailing more resources. However, it is not clear which approaches LGAs actually utilise. This article reports results of a survey designed to discover the level of detail considered practical by British LGAs (n = 34). Results show that resource scarcity is important, with particular importance attached to EM reusability and convenient input data sources. Most LGA EMs use traffic variable inputs (predominantly traffic flow and traffic average speed), with this approach being the best-fit for LGA resources. Link-by-link sources of data rated highly for convenience are road traffic models and urban traffic control systems

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

Multi-level Safety Performance Functions For High Speed Facilities

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users

Exploring the Potentials of Using Crowdsourced Waze Data in Traffic Management: Characteristics and Reliability

Real-time traffic information is essential to a variety of practical applications. To obtain traffic data, various traffic monitoring devices, such as loop detectors, infrastructure-mounted sensors, and cameras, have been installed on road networks. However, transportation agencies have sought alternative data sources to monitor traffic, due to the high installation and maintenance cost of conventional data collecting methods. Recently, crowdsourced traffic data has become available and is widely considered to have great potential in intelligent transportation systems. Waze is a crowdsourcing traffic application that enables users to share real-time traffic information. Waze data, including passively collected speed data and actively reported user reports, is valuable for traffic management but has not been explored or evaluated extensively. This dissertation evaluated and explored the potential of Waze data in traffic management from different perspectives. First, this dissertation evaluated and explored Waze traffic speed to understand the characteristics and reliability of Waze traffic speed data. Second, a calibration-free incident detection algorithm with traffic speed data on freeways was proposed, and the results were compared with other commonly used algorithms. Third, a spatial and temporal quality analysis of Waze accident reports to better understand their quality and accuracy was performed. Last, the dissertation proposed a network-based clustering algorithm to identify secondary crashes with Waze user reports, and a case study was performed to demonstrate the applicability of our method and the potential of crowdsourced Waze user reports

Discharge Flow Rate Change Under Rainy Conditions on Urban Motorways

Queue discharge flow is the most frequently observed phenomenon on urban motorways when demand exceeds capacity. Once a queue is formed, congestion arises, and the number of vehicles that can pass from downstream reduces. This reduction phenomenon is defined as the capacity drop and calculated by taking the difference between capacity and discharge flow at a road section. Obviously, this capacity drop exists after an onset of congestion and may increase in relation to weather conditions, such as rain, snow, or fog, which cause longer queues and delays. In this paper, the effect of rain on discharge flows is investigated and compared with sunny days on Istanbul urban motorways. Besides, rain precipitation during congestion is considered and related to discharge flow. Four different motorway sections were analyzed, and up to 37% discharge flow reduction was determined between sunny and rainy conditions. Motorway sections with higher free flow speed (FFS) were found to be more affected by rain, and discharge flow reduction was bigger compared to the section with the lowest FFS. For 1 mm/m2/h of precipitation, the discharge flow is estimated as 1,719 pcu/h/lane when FFS is 84 km/h, and as 1,560 pcu/h/lane if FFS is 104 km/h.</p

Microsimulating Cross-Border Truck Movements between Ontario and the United States: An Application using Connected Vehicle Technology

The land-border crossings between Canada and the United States facilitate over half of the goods transported between the two countries. Since trucks are the primary mode of transportation for the movement of these goods, studying the traffic flows and the characteristics of border crossings is of paramount importance for decision makers, planners and researchers. The province of Ontario is home to the busiest border crossings in Canada including the Ambassador Bridge in Windsor, Ontario and the Blue Water Bridge in Sarnia, Ontario. GPS data collected from a large sample of trucks shows the route choice characteristics for these border crossings. The same dataset also shows the destination locations for these trucks. This thesis utilizes VISSIM, a microscopic traffic simulator, and its dynamic traffic assignment, an imbedded route choice model, to replicate these route choice conditions. Once the model is validated with the shares of flows from the observed (i.e., reference) datasets, the route choice behavior is analyzed under different delay conditions. The research also analyzed the effects of connected vehicle technology, at different penetration rates, on the efficiency of border crossing operations. As the connected vehicles increased in the traffic stream, it was observed that traffic was more streamlined and would switch to use the Blue Water Bridge during the simulation of an incident on Highway 401. The penetration rate was increased in 20% increments and with 100% penetration, 7% of total truck traffic had switched to Blue Water Bridge to travel to their U.S. destination

A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions

Passenger car equivalents of trucks under lane restriction and differential speed limit policies on four lane freeways

Lane restriction for trucks and differential speed limits for trucks and cars are becoming more common and feasible policies to improve the efficiency and safety of a freeway. It is believed that passenger car equivalents for trucks are impacted by these non typical freeway operating conditions, which are not explicitly addressed by the latest edition of the Highway Capacity Manual. Using simulated and real world data an elevated 18- mile four lane freeway was modeled under the restriction policies. The section which was used as a test bed was simulated under various control variables. Some of the control variables used were speed distributions from the field data, truck percentages in the traffic mix and the compliance rate to the restriction policies. The simulated results were compared with the corresponding values in HCM and observations were made which can be used for further research. The simulated results show that the ET values decreases with increase in truck percentages under the influence of the truck restrictions due to “platooning effect” caused due to increase in the truck percentage