Minimizing Waiting Time at Intermediate Nodes for Intercity Public Bus Transportation Saharidis G.K.D. Dimitropoulos Ch. Skordilis E. Saharidis G.K.D.,

Slides:



Advertisements
Similar presentations
Airline Schedule Optimization (Fleet Assignment I)
Advertisements

A Decision Support System for Improving Railway Line Capacity G Raghuram VV Rao Indian Institute of Management, Ahmedabad.
Capacity Studies on Transportation Network Presented by Rakesh Ambre ( ) Under Guidance Of Prof. Narayan Rangaraj.
Introduction to Mathematical Programming
Decision Trees and MPI Collective Algorithm Selection Problem Jelena Pje¡sivac-Grbovi´c,Graham E. Fagg, Thara Angskun, George Bosilca, and Jack J. Dongarra,
Abstract The SEPTA Regional Rail system serves as an important network for the Philadelphia region, moving many commuters during the peak hours on suburb-to-city.
Train platforming problem Ľudmila Jánošíková Michal Krempl University of Žilina, VŠB-Technical University of Ostrava, Slovak Republic Czech Republic.
Lecture 5 Set Packing Problems Set Partitioning Problems
National Institute of Science & Technology TECHNICAL SEMINAR-2004 Dipanwita Dash [1] UNIT COMMITMENT Under the guidance of Mr. Debasisha Jena Presented.
10 December J/ESD.204J Lecture 13 Outline Real Time Control Strategies for Rail Transit Prior Research Shen/Wilson Model Formulation Model Application.
GEOG 111 & 211A Transportation Planning Traffic Assignment.
INTEGRATED DESIGN OF WASTEWATER TREATMENT PROCESSES USING MODEL PREDICTIVE CONTROL Mario Francisco, Pastora Vega University of Salamanca – Spain European.
University Bus Systems: Network Flow Demand Analysis By Craig Yannes University of Connecticut October 21, 2008.
Operations Management
Math443/543 Mathematical Modeling and Optimization
Results Showing the potential of the method for arbitrary networks The following diagram show the increase of networks’ lifetime in which SR I =CR I versus.
NORM BASED APPROACHES FOR AUTOMATIC TUNING OF MODEL BASED PREDICTIVE CONTROL Pastora Vega, Mario Francisco, Eladio Sanz University of Salamanca – Spain.
Supply Chain Design Problem Tuukka Puranen Postgraduate Seminar in Information Technology Wednesday, March 26, 2009.
Order Sequencing in the Automobile Industry Robert Nickel, Winfried Hochstättler Mathematical Foundations of Computer Science Institute of Mathematics.
Advanced Public Transit Systems (APTS) Transit ITS CEE582.
TRIP ASSIGNMENT.
Airline Fleet Routing and Flight Scheduling under Market Competitions
Kick-off meeting 3 October 2012 Patras. Research Team B Communication Networks Laboratory (CNL), Computer Engineering & Informatics Department (CEID),
Airline Schedule Optimization (Fleet Assignment II) Saba Neyshabouri.
Passenger travel behavior model in railway network simulation Ting Li Eric van Heck Peter Vervest Jasper Voskuilen Dept. Of decision and information sciences.
Quadratic Programming Model for Optimizing Demand-responsive Transit Timetables Huimin Niu Professor and Dean of Traffic and Transportation School Lanzhou.
Decision for the location of Intermodal terminals in a rail-road network Anupam Kulshreshtha IIM - Lucknow.
Max-flow/min-cut theorem Theorem: For each network with one source and one sink, the maximum flow from the source to the destination is equal to the minimal.
An Analysis of Intra Airport Transport From A User’s Perspective Allison Davis December 5, 2002.
Dimitrios Konstantas, Evangelos Grigoroudis, Vassilis S. Kouikoglou and Stratos Ioannidis Department of Production Engineering and Management Technical.
1 Network Optimization in Transportation Scheduling Ravindra K. Ahuja Supply Chain and Logistics Engineering (SCALE) Center Industrial & Systems Engineering.
A multi-modal Route Planning approach: a case-study of the city of Trikala Ενότητα 7: Παρουσίαση 6 Γεώργιος Κ.Δ. Σαχαρίδης Χριστόδουλος Ματσίγκος, Λάζαρος.
07/21/2005 Senmetrics1 Xin Liu Computer Science Department University of California, Davis Joint work with P. Mohapatra On the Deployment of Wireless Sensor.
Network Models Tran Van Hoai Faculty of Computer Science & Engineering HCMC University of Technology Tran Van Hoai.
MIT ICAT ICATMIT M I T I n t e r n a t i o n a l C e n t e r f o r A i r T r a n s p o r t a t i o n Virtual Hubs: A Case Study Michelle Karow
SoftCOM 2005: 13 th International Conference on Software, Telecommunications and Computer Networks September 15-17, 2005, Marina Frapa - Split, Croatia.
Probability Theory School of Mathematical Science and Computing Technology in CSU Course groups of Probability and Statistics.
Transit Priority Strategies for Multiple Routes under Headway-based Operations Shandong University, China & University of Maryland at College Park, USA.
Regional Traffic Simulation/Assignment Model for Evaluation of Transit Performance and Asset Utilization April 22, 2003 Athanasios Ziliaskopoulos Elaine.
1 [3] Jorge Martinez-Bauset, David Garcia-Roger, M a Jose Domenech- Benlloch and Vicent Pla, “ Maximizing the capacity of mobile cellular networks with.
Maximum Network Lifetime in Wireless Sensor Networks with Adjustable Sensing Ranges Cardei, M.; Jie Wu; Mingming Lu; Pervaiz, M.O.; Wireless And Mobile.
Network Optimization Problems
Train timetables Time real life problems – Year 4/5.
May 2009TRB National Transportation Planning Applications Conference 1 PATHBUILDER TESTS USING 2007 DALLAS ON-BOARD SURVEY Hua Yang, Arash Mirzaei, Kathleen.
Experimental Evaluation of Real-Time Information Services in Transit Systems from the Perspective of Users Antonio Mauttone Operations Research Department,
Designing Games for Distributed Optimization Na Li and Jason R. Marden IEEE Journal of Selected Topics in Signal Processing, Vol. 7, No. 2, pp ,
Measuring rail accessibility using Open Data Elena Navajas-Cawood.
1 Planning Base Station and Relay Station Locations in IEEE j Multi-hop Relay Networks Yang Yu, Seán Murphy, Liam Murphy Department of Computer Science.
12/08/ J/ESD.204J1 Real-Time Control Strategies for Rail Transit Outline: Problem Description and Motivation Model Formulation Model Application.
Rough-Cut Capacity Planning in SCM EIN 5346 Logistics Engineering Fall, 2015.
Times are discretized and expressed as integers from 1 to 1440 (minutes in a day). set of stations set of trains set of stations visited by train j The.
WHAT IS OPTIMIZATION? Optimization is a mathematical discipline that concerns the finding of minima and maxima of functions, subject to so-called constraints.
Balanced Billing Cycles and Vehicle Routing of Meter Readers by Chris Groër, Bruce Golden, Edward Wasil University of Maryland, College Park American University,
The integrated dial-a-ride problem with timetabled fixed route service Marcus Posada, Henrik Andersson and Carl Henrik Häll.
Rough-Cut Capacity Planning in SCM Theories & Concepts
CPH Dr. Charnigo Chap. 11 Notes Figure 11.2 provides a diagram which shows, at a glance, what a neural network does. Inputs X 1, X 2,.., X P are.
Ken-ichi TANAKA Department of Management Science,
The Passenger View David Beer Passenger Executive.
Aircraft Landing Problem
A Multi-Airport Dynamic Network Flow Model with Capacity Uncertainty
Rough-Cut Capacity Planning in SCM Theories & Concepts
Karen Tsang Bureau of Transport Statistics Department of Transport
A Modeling Framework for Flight Schedule Planning
The Train Driver Recovery Problem – Solution
Introduction to Operations Research
Project Presentation   For
Planning the transportation of elderly to a daycare center
Javad Ghaderi, Tianxiong Ji and R. Srikant
Chapter 5 Transportation, Assignment, and Transshipment Problems
Chapter 6 Network Flow Models.
Presentation transcript:

Minimizing Waiting Time at Intermediate Nodes for Intercity Public Bus Transportation Saharidis G.K.D. Dimitropoulos Ch. Skordilis E. Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.1 ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΙΑΣ

Introduction The purpose of this research is to create a suitable timetable for intercity buses, departing from various nodes of the network, such that the total waiting time of passengers at intermediate nodes is minimized. Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.2

Description of the problem By using the current infrastructure of the public bus companies in Greece (dubbed KTEL), we tried to reform the bus schedule, for better service of passengers who use intermediate nodes to reach their destination. In many cases, the waiting time at these nodes is very long. The reason for this is that there are interconnections of various itineraries, which increments the difficulty of a suitable time schedule. Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.3

Formulation of the problem The problem is formulated as a mixed integer linear program. The objective function is the minimization of the sum of waiting times for every intermediate node of the network Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.4

Formulation of the problem Assumptions: 1.Steady travel time between nodes 2.Steady number of itineraries (routes) between connected nodes 3.Sufficient parking space in bus terminals (no bottleneck) 4.Generally, parameters describing the problem are considered steady Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.5

Decision variables Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.6

Decision variables Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.7

Illustration of the problem Saharidis G.K.D., Dimitropoulos Ch., Skordilis E. 8 Το σχήμα περιλαμβάνει 3 παράλληλες γραμμές που αναπαριστούν τους κόμβους αναχώρησης, άφιξης και τον ενδιάμεσο. Το χρησιμοποιούμε για να δείξουμε πως υπολογίζουμε τον χρόνο αναμονής στον ενδιάμεσο.

Sets, Parameters Saharidis G.K.D., Dimitropoulos Ch., Skordilis E. 9

Sets, Parameters Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.10

Constraints Constraint (1) defines the combination of routes to and from an intermediate node as active or inactive, based on whether the bus from the intermediate node has already left or not Saharidis G.K.D., Dimitropoulos Ch., Skordilis E. 11

Constraints Constraint (2) calculates the waiting time between the arrival on each intermediate node and the first two available departing routes from that node towards the destination node. Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.12

Constraints Constraint (3) assures that a bus departing from any node will return to this node before and after a certain time Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.13

Constraints Constraint (4) assures that each bus departs after another. Constraint (5) defines the time window in which a bus must depart Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.14

Extensions Furthermore, an extension regarding high priority times is introduced. By using this extension bus departures are gravitated towards certain times. Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.15

Extensions Constraints (7) and (8) calculates the time difference between the departing time of a route and the time where a high passenger demand occurs. Constraint (9) defines the number of buses affected by these high priority times. Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.16

Objective function Minimization of the waiting time for all available buses departing from the intermediate node Using the penalty factor : Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.17

Case study The formulation was applied to the intercity bus network of the island of Crete. Numerical data for Crete network:  122 nodes total.  13 of these nodes considered intermediate.  Number of routes between nodes varies from 2 to 25. Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.18

Case study We will examine three different cases:  Case 1 : Existing bus timetable (benchmark)  Case 2 : Minimizing waiting time without penalty factor  Case 3 : Minimizing waiting time with penalty factor to better approach the existing bus timetables Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.19

For the penalty factor we introduced five high priority times, each corresponding to five time zones Saharidis G.K.D., Dimitropoulos Ch., Skordilis E Minute of the day: Zone 1 Zone 2 Zone 3Zone 4 Zone 5 Case study High Priority Time:

Case study For each time zone, the number of buses affected by the high priority time was set by the number of buses departing, based on the existing bus timetable This also applies to the weight factor of the penalty cost in the objective function Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.21

Results Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.22 Total Waiting Time (%)*Number of Active Routes (%)*Average Waiting Time (%)* Case 11,994, Case 2493, Case 3925, *Percentage of decrease over existing timetable (Case 1).

Results Distribution of waiting times : Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.23 Distribution of itineraries :

Conclusions For all the examined cases there is a clear reduction of the waiting times The use of a penalty factor (Case 3)results in an increased waiting time compared to not using it (Case 2) However, it is greatly reduced compare to the existing bus timetable Using of a penalty factor (Case 3) leads to timetable more suitable for realistic cases, giving passengers more choices considering their departure from intermediate nodes Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.24

References Steer Davies Gleave. Study of passenger transport by coach. Publication TREN/E1/ European Commission, Ceder, A, Golany B, Tal O. (2001) Creating Bus Timetables with Maximal Synchronization. Transportation Research Part A. 35: Eranki, A. “A model to create bus timetables to attain maximum synchronization considering waiting times at transfer stops”. Master’s thesis. Department of Industrial and Management Systems Engineering, University of South Florida, Ibarra-Rojas, O. J., and Y. A. Rios-Solis. “Synchronization of bus timetabling”. Transportation Research Part B, Vol. 46, 2012, pp Hall R., Dessouky M. and Lu Q. “Optimal Holding Times at Transfer Stations”. Computer and Industrial Engineering. Vol. 40, 2001, pp Bussieck M. R., Winter T., and Zimmermann U. T. “Discrete Optimization in public rail transport”. Mathematical Programming. Vol. 79, Issue 1-3, 1997, pp Goverde R. M. P. “Synchronization Control of Scheduled Train Services to Minimize Passenger Waiting Time”. Transport, Infrastructure and Logistics, Proceedings 4 th TRAIL Congress, Chen D. and Wu K. “Research on Optimization Model and Algorithm of Initial Schedule of Intercity Passenger Trains based on Fuzzy Sets”. Journal of Software, Vol. 7, No. 1, 2012, pp Reinhardt L. B., Clausen T., and Pisinger D. “Synchronized dial-a-ride transportation of disabled passengers at airports”. European Journal of Operational Research. Vol. 225, 2013, pp Wong R. C. W., Yuen T. W. Y., Fung K. W. and Leung J. M. Y. “Optimizing Timetable Synchronization for Rail Mass Transit”, Transportation Science, Vol. 42, No. 1, 2008, pp Saharidis G.K.D., Dimitropoulos Ch., Skordilis E.25