Operating speed of vehicles during rainfall at night: case study in Pontian, Johor

Speed is one of the important parameters of traffic flow that can be used to determine the performance of driver’s behaviour under various scenarios. Changes in weather conditions caused changes in drivers’ speed due to various reasons. In Malaysia, there is slightly high number of road accidents at night. Rainfall at night does not only cause poor visibility to drivers, but it also gives a sense of insecurity especially as there is a significant reduction in the visibility of the object in front. Improper road conditions can worsen the situation, for example, rainfall can cause water accumulation on road surfaces which can increase skidding problem, potholes and hydroplaning effect. In relation to these situations, hence it is crucial to understand how the abrupt situation affect response of driver’s in terms of the macroscopic behaviour. These unpredictable environmental changes seem like portraying a very unpleasant journey for drivers especially to travel under rainfall condition at night. Therefore, there is a need to observe how individual vehicles react in terms of speed adjustment and response to the different rainfall intensities downpour at night. Hence this study was conducted to determine the impact of different rainfall intensities at night on vehicles’ speed. Traffic data was obtained using automatic traffic counter at a cross section of a road at Pengkalan Raja, Pontian for about three months during monsoon season. Rainfall data report was obtained from the Department of Drainage and Irrigation, Pontian. From this study, it was found that there is a speed reduction from the dry condition regardless of rainfall intensities at night. As rainfall intensities at night higher, the speed reduction is higher as well except for heavy rainfall condition. The mean speed, 15th percentile and 85th percentile of vehicles decrease with the increase in rainfall intensity at night. It can be concluded that rainfall have impact on vehicle’s speed irrespective of their intensities. Findings from this study can be used to help local authorities and transport planners in planning an efficient traffic management system for a safer travel experience to road users in Malaysia

Operating speed of vehicles during rainfall at night: case study in Pontian, Johor

Speed is one of the important parameters of traffic flow that can be used to determine the performance of driver’s behaviour under various scenarios. Changes in weather conditions caused changes in drivers’ speed due to various reasons. In Malaysia, there is slightly high number of road accidents at night. Rainfall at night does not only cause poor visibility to drivers, but it also gives a sense of insecurity especially as there is a significant reduction in the visibility of the object in front. Improper road conditions can worsen the situation, for example, rainfall can cause water accumulation on road surfaces which can increase skidding problem, potholes and hydroplaning effect. In relation to these situations, hence it is crucial to understand how the abrupt situation affect response of driver’s in terms of the macroscopic behaviour. These unpredictable environmental changes seem like portraying a very unpleasant journey for drivers especially to travel under rainfall condition at night. Therefore, there is a need to observe how individual vehicles react in terms of speed adjustment and response to the different rainfall intensities downpour at night. Hence this study was conducted to determine the impact of different rainfall intensities at night on vehicles’ speed. Traffic data was obtained using automatic traffic counter at a cross section of a road at Pengkalan Raja, Pontian for about three months during monsoon season. Rainfall data report was obtained from the Department of Drainage and Irrigation, Pontian. From this study, it was found that there is a speed reduction from the dry condition regardless of rainfall intensities at night. As rainfall intensities at night higher, the speed reduction is higher as well except for heavy rainfall condition. The mean speed, 15th percentile and 85th percentile of vehicles decrease with the increase in rainfall intensity at night. It can be concluded that rainfall have impact on vehicle’s speed irrespective of their intensities. Findings from this study can be used to help local authorities and transport planners in planning an efficient traffic management system for a safer travel experience to road users in Malaysia

Risk analysis of the disruption to urban transport networks from pluvial flooding

PhD ThesisCities are increasingly vulnerable to damage and disruption from adverse-weather events, due to their high concentration of people and assets. To improve engineering and planning decisions in the face of complex interactions between climate hazards, infrastructure and actors within the urban system requires novel analytical tools and methodologies. This research therefore takes a systems approach to developing an integrated framework to model the impact of surface water flooding on the transport network before using this to explore the effectiveness of potential adaptation options to increase urban resilience. The framework calculates delays in travel times by coupling a hazard model to both a hydraulic model and traffic network simulations. The hazard model was approximated for current climate by obtaining intensities for rainfall events with different return periods using the Flood Estimation Handbook (FEH methodology). These rainfall intensities were converted to flood depths over the region of interest using a dynamic flood model (CityCAT). Spatial flood footprints obtained from the model were integrated over the road network to identify affected transport corridors. To calculate the reductions in vehicle speeds due to standing water on these corridors, a new depth-disruption function (i.e. relates depth of flood water to safe vehicle speed) was developed. This was used to estimate reductions in the speeds of individual vehicles which drive a macro-transport network model that has been developed to calculate city-wide travel times and subsequently how these change due to flooding. Pre-event and post-event travel times of commuters are compared, in order to quantify the impact of flooding on network performance, and assess the effectiveness of urban interventions at managing this risk. The framework has been demonstrated in Newcastle-upon-Tyne (UK) using publicly available data and verified through available historical data. With no adaptation of the transport system, a 1 in 200 year rainfall event increases travel times by more than 50%, with an associated economic impact of over £220,000. Adaptation measures contribute significantly to flood alleviation. Application of a risk-based ‘criticality assessment’ has been shown to enable effective targeting of grey (traditional engineering) adaptation, Page ii and in this case installation of flood management measures at the top six most ‘critical’ locations can reduce net present flood risk by 41% over a 10 years timeframe. This compares to similar reduction (38%) for a green adaptation strategy. The green strategy provides a city-wide flood depth reduction, and it does not represent an economically-feasible option. Green infrastructure also provides additional co-benefits, such as enhanced biodiversity and air quality improvements, deployment of green infrastructure at a city-wide scale would require an unprecedented scale, and high cost, of intervention. Balancing grey and green interventions offers the most effective strategy to manage flood risk to transport disruption. Combining flood modelling and transport network analysis is shown to improve engineering decision-making and enable the prioritisation of adaptation investments in urban areas. The findings and the methodology are of interest to transport policy analysts, planners, economists and engineers, as well as communities affected by disruptive events

Using Microsimulation to Estimate the Impact of Transportation Improvements and Operational Policy Changes on Travel Time Reliability

Traditionally, traffic engineers have designed roadway networks and operational strategies to manage congestion and minimize delays during the peak demand period for some “average” or “typical” day. However, increasingly, there is concern about not only the average traffic conditions along a route (during some period of the day), but also about the variability of the required time to traverse the route. Travel times vary as a function of the departure time according to relatively predictable changes in the traffic demands (i.e. travel times are longer during the peak commuting periods than during off peak periods). However, the time to complete the same trip at the same departure time also varies from day to day. The variability of travel time, and the associated additional costs, has introduced another performance measure in transportation engineering called travel time reliability (TTR). Travel time reliability has gained significant attention among the transportation researchers and practitioners recently. In this research, we aimed to implement traffic microsimulation models in order to model travel time reliability and finally to incorporate it into the alternative comparison. The contribution areas of this research are explained briefly in the following paragraphs. Previous work that has examined the impact of weather on the characteristics of the speed-flow-density relationship has defined the weather conditions a priori and then attempted to determine the macroscopic traffic stream characteristics for these categories. However, for the purposes of modeling travel time reliability, it is necessary to only capture those weather conditions for which the associated macroscopic characteristics are statistically different. In this research we develop a technique to distinguish distinct weather categories through an innovative method. Also, the process of determining macroscopic traffic stream characteristics requires the calibration of a macroscopic speed-flow-density model to field data. In employing this approach, we observed that the errors associated with the estimated parameters are impacted by the number and distribution of the observation points that used to calibrate the model. Therefore, we developed models to estimate the corresponding errors of the estimated traffic parameters and found that for most practical applications, the estimation of the jam density is most sensitive to the distribution of the calibration data. As a result, we suggested some specific conditions for which the jam density value should be assumed a priori rather than calibrated on the basis of the available field data. We additionally wanted to be able to model specific weather categories. We knew the traffic flow parameters of those weather conditions from the field data and we wanted the same traffic characteristics to be simulated in the traffic microsimulation model. Therefore, we proposed and evaluated a method to map the traffic flow characteristics to the TMM input parameters. The model developed in this research is not only applicable to simulate different weather categories, but also can be used to simulate any traffic condition -within the acceptable range of the model- when the traffic flow parameters are known. Furthermore, we aimed to monetize travel time (un)reliability. To do this we have adopted the unreliability cost in terms of the costs of arriving early or arriving late. This approach has been widely used to quantify the costs of unreliability of public transport system; however, for road transport, this construct requires that we know the scheduled travel time which, from the user’s perspective is the anticipated travel. We carried out a stated preference survey to estimate the anticipated travel time based on the travel time distribution. On the basis of the survey responses, we proposed two models in which travelers ignore unusually long travel times when determining their anticipated travel time. Finally, we incorporated all of these findings to create an approach to quantify the cost of travel time (un)reliability using traffic microsimulation tools. We demonstrate this approach to evaluate two road improvement alternatives. We used the traffic simulation model VISSIM to compare these two alternatives based on the travel time cost and travel time reliability cost together

Proceedings of the 3rd International Conference on Models and Technologies for Intelligent Transportation Systems 2013

Challenges arising from an increasing traffic demand, limited resource availability and growing quality expectations of the customers can only be met successfully, if each transport mode is regarded as an intelligent transportation system itself, but also as part of one intelligent transportation system with “intelligent” intramodal and intermodal interfaces. This topic is well reflected in the Third International Conference on “Models and Technologies for Intelligent Transportation Systems” which took place in Dresden 2013 (previous editions: Rome 2009, Leuven 2011). With its variety of traffic management problems that can be solved using similar methods and technologies, but with application specific models, objective functions and constraints the conference stands for an intensive exchange between theory and practice and the presentation of case studies for all transport modes and gives a discussion forum for control engineers, computer scientists, mathematicians and other researchers and practitioners. The present book comprises fifty short papers accepted for presentation at the Third Edition of the conference. All submissions have undergone intensive reviews by the organisers of the special sessions, the members of the scientific and technical advisory committees and further external experts in the field. Like the conference itself the proceedings are structured in twelve streams: the more model-oriented streams of Road-Bound Public Transport Management, Modelling and Control of Urban Traffic Flow, Railway Traffic Management in four different sessions, Air Traffic Management, Water Traffic and Traffic and Transit Assignment, as well as the technology-oriented streams of Floating Car Data, Localisation Technologies for Intelligent Transportation Systems and Image Processing in Transportation. With this broad range of topics this book will be of interest to a number of groups: ITS experts in research and industry, students of transport and control engineering, operations research and computer science. The case studies will also be of interest for transport operators and members of traffic administration