Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application

Road traffic crashes are becoming increasing concerns in many countries. In Europe, many efforts have been devoted to improve road traffic safety yet the important target of halving the number of yearly road deaths in 2010 could not be achieved in many European countries. Among different road types, motorways are safe by design yet crashes if occur would be severe due to high speed practiced. If motorway traffic crash risk could be identified, lives could be saved and severity could be reduced. For this objective, the current thesis aims to establish a methodology for developing models capable of identifying real-time traffic crash risk on motorways. A real-time MotorwaY Traffic Risk Identification Model (MyTRIM) is developed for a study site on motorway A1 in Switzerland. MyTRIM is tested, validated with real data. Three types of historical data altogether available at the study site are used for developing MyTRIM. The data include individual vehicle traffic data collected from double loop traffic detectors, meteorological data from meteorological station located near the study site, and a crash database containing crashes recorded by the police. Based on crash time, pre-crash data representing traffic and meteorological conditions leading to crashes are extracted. Similarly, non-crash data representing traffic and meteorological conditions that are unrelated with crashes are also extracted. As crashes are rare events, a methodology for sampling non-crash data comparable with pre-crash data is developed using clustering – classification basis: non-crash data are clustered into groups; pre-crash data are classified into obtained groups; pre-crash and non-crash data within one group are similar and therefore, comparable. Each group is called a traffic regime. Under each traffic regime, a regime-based Risk Identification Model (RIM) is developed to differentiate pre-crash and non-crash data. Given a new datum, regime-based RIM must be able to classify the datum into pre-crash or non-crash. As a result of the model development, variables which are important for the differentiation are also identified. These important variables can be potential for implementing countermeasures to prevent the risk from ending up with a crash. MyTRIM is developed based on the outputs from regime-based RIM. MyTRIM memorizes the latest risk evolution to predict whether there is crash risk in the coming time interval. Regime-based RIM and MyTRIM are tested and validated using real data. Results show that regime-based RIM and MyTRIM perform with high accuracy. The output of MyTRIM can be useful for traffic operators as an input for actively managing the traffic. The developed methodology can be applied for any motorway traffic sections where similar data are available

INCORPORATING SPEED INTO CRASH MODELING FOR RURAL TWO-LANE HIGHWAYS

Rural two-lane highways account for 76% in mileages of the total paved roads in the US. In Kentucky, these roads represent 85 % of the state-maintained mileages. Crashes on these roads account for 40% of all crashes, 47% of injury crashes, and 66% of fatal crashes on state-maintained roads. These statistics draw attention to the need to investigate the crashes on these roads. Several factors such as road geometries, traffic volume, human behavior, etc. contribute to crashes on a road. Recently, studies have identified speed as one of the key factors of crashes as well as the severity associated with them and indicated the need to incorporate speed into predicting crashes and severity. Such studies are limited for rural two-lane highways due to the lack of measured speed data in the past. This study fills this gap by utilizing widely available measured speed data on these roads and investigates the relationship between speed and crashes on rural two-lane highways. This study collected crash, speed, traffic, and road geometric data for rural two-lane highways in Kentucky. Particularly for the speed, this study utilized GPS-based probe data. The speed data was integrated with the crash data and road attributes for the rural two-lane highways. This study utilized the speed measures directly calculated from the measured speed data and evaluated the effect of speed on the crashes of these roads. At first, this study investigated the effect of speed by incorporating average speed along with traffic volume and length in the crash prediction model for total number of crashes. A zero-inflated negative binomial model was utilized to account for the overdispersion from excess zero crashes in the dataset. From the model, a negative relationship was identified between average speed and number of crashes. One possible explanation is that rural two-lane roads with higher speeds tend to be those main corridors with better geometric conditions. Furthermore, the significance of speed in the model varies with the operating speed on these roads. This suggested considering speed as a categorizer to develop separate models for different speed ranges. Separating models based on speed provided improved prediction performance compared to an overall model. Operating speed often reflects geometric conditions. Therefore, this study also evaluated how the change in the 85th percentile speed from one section to another road section affects the crashes of a road. The analysis showed that more crashes tend to occur when the 85th percentile speed differential between consecutive segments increases. However, further investigation showed that speed differential may not be a suitable indicator of identifying the locations with a high risk of crashes, rather it can be applied for design improvement of the roads. Later, this study investigated spatial heterogeneity of the effect of speed in addition to other factors utilizing a geographically weighted regression model. The model accounted for the geographical location of the data and helped to investigate the spatially varying effect of speed. The results from this model showed that the significance of speed can vary at different locations, which is not observed in the global model. In some regions, speed actually reflects the local geometric conditions of the roads. On the road with poor geometric conditions, crashes tend to be higher. The safety improvement strategies for these roads can focus on improving the geometric conditions such as providing shoulders, realigning the sharp curves, etc. Furthermore, speed seemed to increase crashes in some locations with good geometric conditions and low traffic volume. Speed was indeed a critical factor for these locations and safety countermeasures should be recommended considering the operating condition. Utilizing measured speed data, this study also explored the effect of speed separately on KABC and PDO crashes for these roads. Separate models were developed for KABC and PDO crashes using a zero-inflated Poisson model form. Results from the models showed that speed had a positive relationship with KABC crashes, but a negative relationship with PDO crashes. For the KABC crashes, more KABC crashes tend to occur on high-speed roads. In contrast, PDO crashes tend to be higher on low-speed roads with poor geometric conditions. Furthermore, this study separated the models for each severity level using speed as a categorizer. The models developed at individual speed ranges revealed a varying effect of speed over the different speed ranges of these roads. For example, speed had a positive effect on KABC crashes of low and medium-speed roads, whereas it had a negative influence on crashes of high-speed roads. Further investigation of the study data showed that most of the low and medium-speed roads had poor geometric conditions (narrow shoulder and lane widths with the presence of sharp curves), whereas, high-speed roads had standard geometric conditions. Especially on low-speed roads, it is understandable that a crash can be severe when speed goes up under such restrictive geometric conditions of the roads. In contrast, on high-speed roads, the number of severe crashes tends to be low under standard geometric conditions. Additionally, separating models considering speed ranges provided 19% and 6.5% improvement respectively for KABC and PDO crashes compared to the overall models. Such models can help the agencies to adopt strategies for minimizing crashes at different severity levels based on the speed condition of the road. This study further looked at the effect of speed using Random Forest model since it can deal with multicollinearity between explanatory variables and requires no assumptions on the functional form. After including all the traffic and geometric variables in the model, speed showed 11.5% importance. Compared to the traditional count model, the model provided a better fit with an improved performance of 13%. For better predictability, planning level safety analysis can utilize such machine learning model

Determination and measurement of factors which influence propensity to cycle to work

2.89% of the UK population cycled for the journey to work as measured by the census in 200I. This percentage is similar to the percentage from the 1991 census and indicates a levelling off in the decline that had been seen in the previous two decades in bicycle use for the journey to work, but does not demonstrate any increase in line with policy aspirations. Choice is a complex issue and related to a wide range of factors including socio-economic variables and the nature of transport infrastructure and the physical geography of an area. As well as the rational and measurable factors, there are many much more complex and subtle factors including the influences of culture and social norms. Changes to behaviour probably take an extended period of time and require a range Qf conditions to be appropriate before a positive choice can be made. Waldman (1977) undertook the last countrywide aggregate study of the variation in use of the bicycle for the journey to work, but a number of the variables he constructed were measured inappropriately, not the least of which was his measure for "danger", which he recommended for further study. It is widely considered that perception of risk from motor traffic is a reason why many people do not currently use the bicycle. This is only one measurable attribute and European bicycle planners consider network coherence, directness, attractiveness and comfort as other equally important issues when designing schemes to promote bicycle use. This research has used primary data collected on perceptions of risk. The particular contribution of the research is in the development of a methodology for the determination of perception of risk for a whole journey, including routes and junctions, and the extension of this methodology to create a measure for risk at an area wide level. Measures that have been found to be significant in relation to the use of the bicycle for the journey to work are car ownership, socio-economic classification, ethnicity, distance to work, condition of the highway pavement, highway network density and population density, hi lIiness, rainfall and mean temperature. In addition the length of bicycle lane, length of bus lane and length of traffic free route have also been found to be important in so far as it influences the perception of risk, which in turn influences the level of bicycle use. The length of route that is signed has also been found to be important. In a sample of four districts for which appropriate data is available, a seven fold increase in route length with cycle facilities, or signed route, would create conditions suitable for an increase in cycle use for the journey to work by a factor of the order of two. An elimination of highways with negative residual life would create conditions suitable for an increase of 10% in the number of bicycle trips for the journey to work

Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022

The 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS) was held in Dresden, Germany, from November 30th to December 2nd, 2022. Organized by the Chair of Traffic Process Automation (VPA) at the “Friedrich List” Faculty of Transport and Traffic Sciences of the TU Dresden, the proceedings of this conference are published as volume 9 in the Chair’s publication series “Verkehrstelematik” and contain a large part of the presented conference extended abstracts. The focus of the MFTS conference 2022 was cooperative management of multimodal transport and reflected the vision of the professorship to be an internationally recognized group in ITS research and education with the goal of optimizing the operation of multimodal transport systems. In 14 MFTS sessions, current topics in demand and traffic management, traffic control in conventional, connected and automated transport, connected and autonomous vehicles, traffic flow modeling and simulation, new and shared mobility systems, digitization, and user behavior and safety were discussed. In addition, special sessions were organized, for example on “Human aspects in traffic modeling and simulation” and “Lesson learned from Covid19 pandemic”, whose descriptions and analyses are also included in these proceedings.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the FutureDas 4. Symposium zum Management zukünftiger Autobahn- und Stadtverkehrssysteme (MFTS) fand vom 30. November bis 2. Dezember 2022 in Dresden statt und wurde vom Lehrstuhl für Verkehrsprozessautomatisierung (VPA) an der Fakultät Verkehrswissenschaften„Friedrich List“ der TU Dresden organisiert. Der Tagungsband erscheint als Band 9 in der Schriftenreihe „Verkehrstelematik“ des Lehrstuhls und enthält einen Großteil der vorgestellten Extended-Abstracts des Symposiums. Der Schwerpunkt des MFTS-Symposiums 2022 lag auf dem kooperativen Management multimodalen Verkehrs und spiegelte die Vision der Professur wider, eine international anerkannte Gruppe in der ITS-Forschung und -Ausbildung mit dem Ziel der Optimierung des Betriebs multimodaler Transportsysteme zu sein. In 14 MFTS-Sitzungen wurden aktuelle Themen aus den Bereichen Nachfrage- und Verkehrsmanagement, Verkehrssteuerung im konventionellen, vernetzten und automatisierten Verkehr, vernetzte und autonome Fahrzeuge, Verkehrsflussmodellierung und -simulation, neue und geteilte Mobilitätssysteme, Digitalisierung sowie Nutzerverhalten und Sicherheit diskutiert. Darüber hinaus wurden Sondersitzungen organisiert, beispielsweise zu „Menschlichen Aspekten bei der Verkehrsmodellierung und -simulation“ und „Lektionen aus der Covid-19-Pandemie“, deren Beschreibungen und Analysen ebenfalls in diesen Tagungsband einfließen.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the Futur