A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

Abstract

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach

Flux through a Markov chain

“…It is out of the scope of this work to develop in detail the comparison of our results with real data. Instead by comparing our simulations with the ones made in [30], [31], we hope that it is possible to confirm the relevance of our paradigm. It is natural to introduce in this context a directed graph (G, V, E) where nodes V correspond to junctions and directed edges to connecting roads.…”

“…Here we modify a known road network Markov model, [30], [31], in order to introduce external sources and sinks of vehicles. It is out of the scope of this work to develop in detail the comparison of our results with real data.…”

Mitigating the Impact of Congestion Minimization on Vehicles’ Emissions in a Transportation Road Network

“…It is possible to use the fundamental flow relation and the BPR formula to derive a relationship between traffic density D and vehicle speed S. This results in the polynomial relationship defined in (15), where S f is the average vehicles speed in free flow conditions, and D c is traffic density at road's capacity (when v=c). However, deriving a closed form general solution to this polynomial relationship is not possible due to the resulting quintic polynomial function.…”

“…-a(1+a) b ( ) b = 0 (15) We overcame this obstacle by evaluating the BPR formula at many v values covering the range of possible flow values needed in the optimization model. The resulting travel times at various flow values were used to calculate corresponding D and S values.…”

Railway Assets: A Potential Domain for Big Data Analytics

“…It applied for Istanbuls automated fare collection system and pricing for BRT-Bus Rapid Transit line planning with visualization metrics to obtain better recommendations for consumers [6]. Using Markov-chain approach, a multi-modal transport network in London was developed with better information clusters for efficiency of transport [7]. City Intelligent Energy Network used statistical data including city economy, construction, population, and different energy parameters to develop the comprehensive model for low-carbon emissions [8].…”