Foundation technology for developing an autonomous Complex Dwell-time Diagnostics (CDD) Tool

© 2015 ATRF, Commonwealth of Australia. All rights reserved. As the demand for rail services grows, intense pressure is placed on stations at the centre of rail networks where large crowds of rail passengers alight and board trains during peak periods. The time it takes for this to occur — the dwell-time — can become extended when high numbers of people congest and cross paths. Where a track section is operating at short headways, extended dwell-times can cause delays to scheduled services that can in turn cause a cascade of delays that eventually affect entire networks. Where networks are operating at close to their ceiling capacity, dwell-time management is essential and in most cases requires the introduction of special operating procedures. This paper details our work towards developing an autonomous Complex Dwell-time Diagnostics (CDD) Tool — a low cost technology, capable of providing information on multiple dwell events in real time. At present, rail operators are not able to access reliable and detailed enough data on train dwell operations and passenger behaviour. This is because much of the necessary data has to be collected manually. The lack of rich data means train crews and platform staff are not empowered to do all they could to potentially stabilise and reduce dwell-times. By better supporting service providers with high quality data analysis, the number of viable train paths can be increased, potentially delaying the need to invest in high cost hard infrastructures such as additional tracks. The foundation technology needed to create CDD discussed in this paper comprises a 3D image data based autonomous system capable of detecting dwell events during operations and then create business information that can be accessed by service providers in real time during rail operations. Initial tests of the technology have been carried out at Brisbane Central rail station. A discussion of the results to date is provided and their implications for next steps

DEVICES, FRAMEWORKS AND METHODOLOGIES CONFIGURED TO ENABLE AUTOMATED MONITORING AND ANALYSIS OF DWELL TIME

Described herein are devices, frameworks and methodologies configured to enable monitoring and analysis of dwell time in respect of a human conveyance. Embodiments of the invention have been particularly developed for monitoring and analysis of dwell time in respect of trains. In some examples, the technology makes use of depth-sensitive sensor equipment to monitor activity in three dimensions, including train and passenger and activity, thereby to identify artefacts of dwell time events

Minimizing the stop time of private vehicles at intersections with LRT signal priority systems

There are some strategies suggested to improve the performance of intersections and increase the demand for public vehicles by prioritizing them. To this end, several methods have been used such as Transit Signal Priority (TSP) system for Light Rail transit (LRT). LRT signal priority is a timing strategy that gives priority to LRTs at signalized intersections through changing the sequence of phases, extending green time and reducing red time at LRT\u27s phase. In this paper, we propose a model to improve LRT signal priority systems. The developed model minimizes the green extension and red reduction of LRT\u27s phase by estimating an optimal speed for LRTs reaching the stop line. Consequently, the priority of LRTs would be maintained while the performance of private vehicles would be improved by decreasing their stop time

Minimizing the Average Delay at Intersections via Presignals and Speed Control

© 2018 Mina Ghanbarikarekani et al. Urban intersections have been well recognized as bottlenecks of urban transport systems. It is thus important to propose and implement strategies for increasing the efficiency of public and private transportation systems as a whole. In order to achieve this goal, an additional signal could be set up near the intersection to give priority to buses through stopping vehicles in advance of the main intersection as a presignal. It has been increasingly popular in urban cities. While presignals indeed reduce the average delay per traveler, they cause extra stops of private vehicles, which might compromise the overall efficiency, safety, and sustainability. This paper aims to propose a model to improve presignals by reducing the vehicles' number of stops behind the presignals. By applying the method, vehicles would be able to adjust their speed based on traffic conditions as well as buses' speed and approach. Numerical analyses have been conducted to determine the conditions required for implementing this method