Abstract

There is a large number of tools for the simulation of traffic and routes in public transport systems. These use different simulation models (macroscopic, microscopic, and mesoscopic). Unfortunately, these simulation tools are limited when simulating a complete public transport system, which includes all its buses and routes (up to 270 for the London Underground). The processing times for these type of simulations increase in an unmanageable way since all the relevant variables that are required to simulate consistently and reliably the system behavior must be included. In this paper, we present a new simulation model for public transport routes’ simulation called Masivo. It runs the public transport stops’ operations in OpenCL work items concurrently, using a multi-core high performance platform. The performance results of Masivo show a speed-up factor of 10.2 compared with the simulator model running with one compute unit and a speed-up factor of 278 times faster than the validation simulator. The real-time factor achieved was 3050 times faster than the 10 h simulated duration, for a public transport system of 300 stops, 2400 buses, and 456,997 passengers.

Highlights

  • Millions of more people are expected to migrate to urban centers in the following years.The United Nations predicts that for 2050, 66% of the population will live in urban areas [1]

  • We have the Parallel Simulation Core (PSC), which runs in OpenCL

  • We have the Results’ Statistics Module (RSM), which is a Python module that extracts the statistical information related to passengers served, commute times, and simulation performance

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Summary

Introduction

Millions of more people are expected to migrate to urban centers in the following years.The United Nations predicts that for 2050, 66% of the population will live in urban areas [1]. Smart cities target the increasing capacity to collect a large quantity of data from a PTS and process them in real-time (big data processing). This capacity has become the basis for the development of more effective transit modeling and simulation [3]. An efficient transit simulation powers the optimization agents for public transport systems These optimization agents improve the route path, route frequency, number of transfers, the waiting time, bus or train sizes, ticket price, and others. Different studies reduced the wait time by 75% [4], the travel time by 14% [5], and operational cost by 11% [5]

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