A platform for enhanced transportation services GEORGE GATT THESIS PRESENTATION FOR THE MSITT PROGRAM ATHENS INFORMATION TECHNOLOGY MARCH 2015 THESIS ADVISOR DR SOFOKLIS EFREMIDIS

Thesis Objectives Development of a platform for calculation of the optimal route to a user specified destination considering the time schedules of various public transportation means (bus, metro, tram) and the traffic load of the network Design of an Android application that allows users to set their requirements and receive the shortest route at current time as a set of directions to follow Demonstration of the platform with some sample scenarios

GENERAL OUTLINE INTRODUCTION TECHNOLOGIES AND TOOLS ARCHITECTURE AND DESIGN IMPLEMENTATION EXPERIMENTATION FUTURE WORK

INTRODUCTION (I) The problem of finding the optimal time delay route between two points in an urban environment can have many positive implications (reduce travel cost and time, reduce traffic jams, improve tourism services) The user selects an origin address, a destination address, a maximum walking distance between stations and the types of public transportation means The server of the service provider receives the request from the user and computes the shortest path at the time of request by retrieving some dynamic traffic data from the server of the transport provider

INTRODUCTION (II) A time dependent shortest path (TDSP) algorithm is applied for the calculation of the optimal route under certain assumptions The server of the service provider returns its response to the Android client This platform faces some important computational challenges (limit of daily requests to the Google Maps API, many client requests at the server provider, large transportation graphs) Future improvements are possible by a more reliable real-time estimation of the traffic load of the network

FEATURES OF THE ITRAFFIC APPLICATION The application gives a user the choice to configure some parameters and set some preferences for future queries The application gives a user the choice of setting the origin and destination addresses either as a string of characters or by clicking a point on the map The application displays the optimal route as a list of data and as a sequence of line segments on the map

TECHNOLOGIES Android SDK ( Java SE7 ( Java EE7 (JSF, JPA) ( RESTful Web Services ( Google Maps ( Java JSON libraries (

TOOLS Android Studio and AVD manager ( Online tool for geocoding ( Netbeans v.8 IDE ( Glassfish v.4 application server ( MySQL database server (

THE ANDROID ARCHITECTURE

ARCHITECTURE AND DESIGN General Architecture of the iTraffic System Database Model Transport Provider Service Provider Android Client

GENERAL ARCHITECTURE iTraffic Android application (Client) iTraffic Android application (Client) RESTful Web Service on Glassfish (Service Provider) RESTful Web Service on Glassfish (Service Provider) RESTful Web Service for buses (Transport provider) RESTful Web Service for subway (Transport provider) RESTful Web Service for tram (Transport provider) RESTful Web Service for suburban (Transport provider) RESTful Web Service for rail (Transport provider) MySQL

DATABASE MODEL Database model is the same for every type of public transportation The Velocity values are changed randomly by the transport provider in order to simulate the current load of the traffic network

TRANSPORT PROVIDER INTERACTION OF COMPONENTS ON GLASSFISH Glassfish App Server ORM (JPA) MySQL Database Business Layer (EJB) REST Interface (JSON, XML) REST Interface (JSON, XML) Web Interface (JSF) Web Browser Service Provider Service Provider

SERVICE PROVIDER INTERACTION OF COMPONENTS ON GLASSFISH Glassfish App Server REST Interface (JSON) REST Interface (JSON) Shortest Path POJO Initialization Servlet REST Methods Transport Provider Transport Provider Android Client Android Client

TIME DEPENDENT GRAPHS Graphs with time-varying edge weight functions First-in first-out (FIFO) and non-FIFO graphs Examples: road networks (FIFO) for urban trip planning or hazardous material routing, communication networks (non-FIFO) for flow control of packet transmission Definition of TDSP from node 1 to node 2: earliest arrival time path at node 2 starting from node 1 at a given time instance t

AN EXAMPLE

MOST IMPORTANT TDSP ALGORITHMS A. Orda and R. Rom, "Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length", in J. ACM, 37(3), pp , 1990 K. Sung, M. G. Bell, M. Seong, and S. Park, "Shortest paths in a network with time- dependent flow speeds", in European Journal of Operational Research, 121(12), pp. 32–39, 2000 B. Ding, J,X. Yu, and L. Qin, "Finding Time-Dependent Shortest Paths over Large Graphs", in Proceedings of the 11th Int. Conference on EDT: Advances in Database Technology, EDBT' 08, March 25-30, pp , 2008, Nantes, France. K. Androutsopoulos, and K. Zografos, "Solving the k-shortest path problem with time windows in a time varying network", in Operations Research Letters, 36, pp , 2008 M. Omran, "Path Problems in Geographic Information Systems", PhD Thesis, Carleton University, Ottawa, Ontario, 2014

TDSP ALGORITHM AT SERVICE PROVIDER Initialization: (Step 1) Xs=ts, fs=NIL; for every k≠s Yk=∞, Xk=NULL, fk=NIL; j=s; Relaxation: (Step 2) For all neighbors k of j for which Xk=NULL, do: a. Yk = min{ Yk, Xj + Djk(Xj)} b. If Yk changed in Step 2(a), then set fk=j. If all nodes have non-null X-value, then stop. (Step 3) Otherwise, let l be a node for which Xl=NULL and such that Yl ≤ Yk for every k for which Xk=NULL. Set Xl=Yl, j=l, and proceed with Step 2. Initialization: (Step 1) Xs=ts, fs=NIL; for every k≠s Yk=∞, Xk=NULL, fk=NIL; j=s; Relaxation: (Step 2) For all neighbors k of j for which Xk=NULL, do: a. Yk = min{ Yk, Xj + Djk(Xj)} b. If Yk changed in Step 2(a), then set fk=j. If all nodes have non-null X-value, then stop. (Step 3) Otherwise, let l be a node for which Xl=NULL and such that Yl ≤ Yk for every k for which Xk=NULL. Set Xl=Yl, j=l, and proceed with Step 2. This is the TDSP algorithm of Orda & Rom For FIFO graphs the functions Djk(t) are the edge weight functions The simplest but not the most efficient TDSP algorithm ( O( IVI²) ) Returns the shortest paths spanning tree: j  fj Time delay for shortest path from s to j at time ts: Xj-ts

OUR GRAPH MODEL FOR PUBLIC TRANSPORTATION Is a directed graph Is not fully connected At every client request its structure changes The weight function between 2 nodes depends on the daily time schedule of the line and on the traffic load (leg velocities) at request time The vehicles’ waiting time at the stations and the client’s walking velocity are considered constant The graph is implemented using the adjacency list mapping for neighboring nodes and neighboring edge weights The values of leg velocities for future time instances that are needed for Step 2(a) are the same with the values of leg velocities at request time

ANDROID CLIENT The Android application provides a user interface for the clients to state their travel requirements: origin address, destination address, maximum desired walking distance, types of public transportation For the last 2 parameters the client can configure the application The origin and destination addresses can be set as a character string or by clicking on the map The application invokes the REST interface of the service provider using the above parameters The application parses the response and displays the optimal route on the map

ANDROID PROJECT STRUCTURE

EXPERIMENTATION WITH AN AVD

FUTURE WORK Augment the database with the time schedules during the weekend Simulate more accurately the variations of the leg velocities (a) Receive and process real-time location data from drivers and users (b) Process databases of real-time traffic data from the past (c) Use trained models for reliable traffic load representation (d) Combinations of the above Decrease computation time and memory requirements (a) Implement more efficient TDSP algorithms (b) Store locally on the service provider graph data for the road network (c) Use a cloud infrastructure (d) Use the Neo4j graph DBMS in embedded form Support more complex criteria for the route query (a) Return the first k-shortest path routes (b) Enable the use of car driving as a transportation mean (c) Enable the setting of the optimal route request in the scope of a time window (d) Enable dynamic updates of the optimal route from the service provider after each client request