Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data

Abstract

How can urban movement data be exploited in order to improve the flow of traffic within a city? Movement data provides valuable information about routes and specific roads that people are likely to drive on. This allows us to pinpoint roads that occur in many routes and are thus sensitive to congestion. Redistributing some of the traffic to avoid unnecessary use of these roads could be a key factor in improving traffic flow.Many proposed approaches to combat congestion are either static or do not incorporate any movement data. In this work, we present a method to redistribute traffic through the introduction of externally imposed variable costs to each road segment, assuming that all drivers seek to drive the cheapest route. We use a metaheuristic optimisation approach to minimise total travel times by optimising a set of road-specific variable cost parameters, which are used as input for an objective function based on traffic flow theory. The optimisation scenario for the city centre of Tokyo considered in this paper was defined using public spatial road network data, and movement data acquired from Foursquare. Experimental results show that our proposed scenario has the potential to achieve a 62.6% improvement of total travel time in Tokyo compared to that of a currently operational road network configuration, with no imposed variable costs.

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

“…Table 2 lists industrial applications in maritime industries [262,82], transportation [2,13,10,171,199,16], autonomous vehicles [263,228], health [2,178,10], behavior analysis [199,16], manufacturing [216,10,77] and indoor positioning [216,2].…”

“…The results show that the response surface methods (particularly constrained optimization using response surfaces) have consistently good performance under all scenarios. 4.1 Industrial Applications Later on Arp et al [13] worked on the problem of determining best alternative routes in case of congestion or diversions using movement data. They use parametric optimization and network based real-time forecasting for traffic flow en route on a network.…”

“…Comment: The technique mentioned can be used for vehicles moving in manufacturing space [13] Route estimation, collision avoidance…”

“…The application of movement analysis is studied in literature from various point of view in manufacturing as well as other industries. Another industry making use of movement analytics is the transport industry where there are many applications such as identifying consumer behavior that in turn determines the demand of a specific route; collision free paths; route congestion; demand of different paths at different times; and estimating alternate routes [16,199,13,171]. To identify consumer behavior in using specific routes, correct labeling of events plays an important role.…”

An Intelligent Tree Planning Approach Using Location-Based Social Networks Data

“…Most of these approaches have considered studying the network properties of LBSN data without considering how such information can be used in improving urban aspects. Recently, Arp et al [2] have shown how such data can be used in optimising the state of traffic within the city. A recent trend in which private companies make their data available through various "Data for Good" programs helps to advance research in the field.…”