Timetable and Headway Adherence Assessment using the Gini Coefficient and the Lorenz Curve

Adherence to the schedule is of prime importance in public transport. This paper presents a specific application of the Gini coefficient, well known indicator in economics, for the headway adherence assessment. The paper shows that Lorenz curve, which is usually used to define mathematically the Gini coefficient, is a good indicator of the users' waiting time when it is based on the bus schedule. When it is computed on the basis of the ratio of observed headway to the schedule, it is a powerful visual tool that can be used by operators to detect the existence of irregularities on a bus line at a glance. An equation gives, in an idealistic case, the impact of any single traffic disturbance on the Gini coefficient, making this coefficient comprehensive. A detailed analysis is developed, based on the bus proportions according to the headway adherence level. These proportions are obtained from new indices coming from the derivative of the Lorenz curve. The values of these indexes alert the operator of any adherence disturbance. The examination of the Lorenz curve takes more time, but is worthwhile, giving the types of the irregularities The application of these indicators is carried on real-time data from the New Delhi bus network.

Using Traffic Disturbance Metrics to Estimate and Predict Freeway Traffic Breakdown and Safety Events

The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic metrics that can be used for better estimation and prediction of traffic performance. This study examines the use of disturbance metrics in combination with the macroscopic metrics usually used for the estimation of traffic safety and mobility. The disturbance metrics used are the number of oscillations and a measure of disturbance durations in the time exposed time to collision. The study investigates using the disturbance metrics in data clustering for better off-line categorization of traffic states. In addition, the study uses machine-learning based classifiers for the recognition and prediction of the traffic state and safety in real-time operations. The study also demonstrates that the disturbance metrics investigated are significantly related to crashes. Thus, this study recommends the use of these metrics as part of decision tools that support the activation of transportation management strategies to reduce the probability of traffic breakdown, ease traffic disturbances, and reduce the probability of crashes.

A real-time train routing and platforming problem in complex railway stations

This paper studies the real-time trains routing and platforming problem (RT-TRPP) in railway stations that arises from the unreliable arrival times of freight trains, flexible shunting operations and dynamic station layout caused by equipment failure. The feasibility of station timetable is checked before preparing a route for a train or after updating the station layout. If the station timetable is infeasible, the reassignment of trains is triggered. After introducing a problem formulation for the RT-TRPP, we propose an Integer Linear Program (ILP) that strives to minimize the number of conflicting trains. In resulting timetable, directions, arrival and leaving time remain the same with networks timetable to prevent traffic disturbance of neighboring territories. If the resulting timetable is still infeasible, conflicting trains are pointed out with the cause analysis. The method is tested on real-world complex station which receives always the overload of trains’ activities. The optimal full-day solution of 249 trains is obtained within 2 seconds. The efficiency of this method meets the time-critical nature of RT-TRPP.

A Monitoring Approach Based on Fuzzy Stochastic P-Timed Petri Nets of a Railway Transport Network

This paper proposes a monitoring approach based on stochastic fuzzy Petri nets (SFPNs) for railway transport networks. In railway transport, the time factor is a critical parameter as it includes constraints to avoid overlaps, delays, and collisions between trains. The temporal uncertainties and constraints that may arise on the railway network may degrade the planned schedules and consequently affect the availability of the transportation system. This leads to many problems in the decision and optimization of the railway transport systems. In this context, we propose a new fuzzy stochastic Petri nets for monitoring (SFPNM). The main goal of the proposed supervision approach is to allow an early detection of traffic disturbance to avoid catastrophic scenarios and preserve stability and security of the studied railway networks. Finally, to demonstrate the effectiveness and accuracy of the approach, an application to the case study of the Tunisian railway network is outlined.

Overview of road performance on the tsunami evacuation road during the n-COVID19 pandemic

The COVID-19 pandemic has changed the world in various sectors and human activities. Limiting human activity and mobility also has an impact on transportation and traffic. This study aims to calculate the capacity and performance of roads under normal pandemic conditions before PSBB (Large-Scale Social Restrictions) in April 2020 and New Normal in July 2002, as well as predict traffic conditions if the Tsunami disaster hits the city during both periods. Tsunami Evacuation roads in Padang City were selected for analysis. The Indonesian Road Capacity Manual 1997 on urban roads is used as a reference for analyzing road performance indicators. The results showed that; road performance during the PSBB period was better than the New Normal period. The effect of volume and side traffic disturbance factors in the New Normal period makes a significant decrease in performance. Through prediction simulations, if a Tsunami occurs in the two study periods, the analyzed roads can relatively serve evacuation movements. However, the capacity needs to be increased for normal conditions.

Estimated remaining fatigue life of flexible pavements based on the normalized comprehensive area ratio deflection parameter

A lot of pavement deflection data are available that may be utilized as a tool to evaluate the structural capacity of pavement structures at network and project levels. Falling weight deflectometer (FWD) is one of the most widely utilized devices in pavement deflection testing. Under FWD testing, deflections generated at several lateral locations as a result of surface loading application are recorded. One of the major downsides of the static FWD testing is the traffic disturbance due to the required lane closures during testing. As an effort to reduce the amount of the required FWD testing on the network level, this study aims to run an advanced computer simulation analysis to mimic the FWD deflection bowl obtained from the field. The entire simulated FWD deflection bowl was utilized in the development of the new comprehensive pavement deflection bowl area parameters. The tensile strain at the bottom of the asphalt layer was successfully related to the developed normalized comprehensive area ratio parameter ([Formula: see text]) and to the number of load repetitions to fatigue failure. The newly developed parameter was evaluated utilizing data for 35 long term pavement performance sections in Texas. The newly developed [Formula: see text] can be easily implemented and utilized as a tool in any pavement management systems.