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Locomotive Tracking in Satellite Visible and Low Satellite Visible Area
Published in Tanuja Patgar, Devi CS Kavitha, On-Board Design Models and Algorithm for Communication Based Train Control and Tracking System, 2022
Tanuja Patgar, Devi CS Kavitha
The case study of single-train test journey is considered. The test journey path simulation is based on rail line between Madgaon (Goa) to Honavar (Uttara Kannada) with an intermediate stop. Figure 4.2 depicts 12619 Mathysaganda Express realistic train trajectories operated between Madgaon to Honnavar. The graph shows six intermediate main station stops, three bridges and two tunnels along the route. The simulation input data for the case study are: Rail route length = 150 kmDestination station = 150 kmIntermediate stations = 6Train stop time = 3 minutes
Design of Unpowered Railway Vehicles
Published in Simon Iwnicki, Maksym Spiryagin, Colin Cole, Tim McSweeney, Handbook of Railway Vehicle Dynamics, 2019
Anna Orlova, Roman Savushkin, Iurii (Yury) Boronenko, Kirill Kyakk, Ekaterina Rudakova, Artem Gusev, Veronika Fedorova, Nataly Tanicheva
The following characteristics are used to describe the vehicle brake system: Shoe force: The physical value of the force applied from the brake shoe to the wheel tread.Friction coefficient: The friction coefficient between the brake shoe or pad and the wheel tread or brake disc surface. The coefficient is non-linearly dependent on the vehicle speed and the materials of the friction surfaces.Vehicle stop distance: The distance that the single vehicle will travel from its initial speed to a full stop after the emergency brake is applied or the train line brake pipe disconnects. The stop distance of a single vehicle is measured in tests on straight horizontal track.Train stop distance: The distance that the train consisting of identical vehicles will travel from its initial speed to a full stop after the emergency brake is applied. Train stop distance is usually bigger than the vehicle stop distance because the pressure decrease wave needs to travel through the train to reach every vehicle. Train stop distance is highly dependent on track gradient.Skidding: Skidding between the braked wheel and the rail occurs when the brake force is bigger than the wheel-rail traction force. It increases the stop distance and produces wheel and rail defects.
Demand-driven timetable and stop pattern cooperative optimization on an urban rail transit line
Published in Transportation Planning and Technology, 2020
Pan Shang, Ruimin Li, Liya Yang
The planning process for public transportation consists of several consecutive phases. The process begins with network design, typically followed by line planning, timetabling, and vehicle and crew scheduling (Schobel 2012). The train timetable determines the arrival and departure times of trains at each station along an urban rail transit line, which plays an important role in the management and operation of the rail system (Shang et al. 2019). The train stop pattern specifies the train stop plan at each station along the urban rail transit line. Timetable optimization and stop pattern optimization for an urban rail transit line, which are regarded as the most important parts of train management and operations, have often been investigated separately up to now because of the complexity of each problem (Yang et al. 2016).
Trade-off between efficiency and fairness in timetabling on a single urban rail transit line under time-dependent demand condition
Published in Transportmetrica B: Transport Dynamics, 2019
Dewei Li, Tianyu Zhang, Xinlei Dong, Yonghao Yin, Jinming Cao
For the convenience of formulating the problem, several assumptions are made throughout the paper. Passenger boarding on train obeys FCFS (first-come first-service) rules, i.e. passengers who arrive at the station first board the train first. Moreover, when passengers with different OD pairs arrive at a station the same time, they get on the train proportionally.Any station only allows one train to dwell during a short time period. No trains are permitted to overtake at stations or sections.Capacity of each train is assumed the same. Coupling and decoupling in the planning horizon is neglected. Train numbers are not fixed, we only give the maximum number of trains.Train stop strategy is fixed. There is only one kind of train service that stops at all stations.
Approach and application on high-speed train stop plan for better passenger transfer efficiency: the China case
Published in International Journal of Rail Transportation, 2019
In this paper we applied a loop optimization and customer-oriented approach to obtain a better RSTP. The loop process includes five steps: First, an improved Frank-Wolfe algorithm is used to assign passenger flow. Second, the obtained train stop plan is evaluated. Third, the evaluation result is used as a feedback to adjust the train stop plan, a new line plan scheme with new train stop plan is obtained, and another passenger flow distribution is applied. The whole processes will be repeated until we have final RSTP scheme with reasonable PTE. The Beijing-Shanghai HSR network in China was used as a background to test the performance of the methodology and algorithm. The results showed that the approach formulated in this paper provided good solutions to the objectives and requirements. The whole number of transfer passengers was reduced by 21.72% with a corresponding increase of 10.27% in the number of direct passenger. Also, the average transfer passenger travel rate was reduced by 4.76% after the RSTP optimization. The running train passenger loading became reasonable because only 21 trains missed the 30% limitation about the proportion of transfer passenger number and the whole carried passenger number after optimization, and the transfer passenger amount distribution in each station with different transferring supporting ability (facilities, equipment and organization) has become suitable and acceptable.