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 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

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

A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting

© 2018 IEEE. Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects' sizes in images and serious occlusion among people, make it still a challenging problem. In this paper, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. Our method takes advantages of contextual information to provide more accurate and adaptive density maps and crowd counting in a scene. Extensively experimental evaluation is conducted using different benchmark datasets for object-counting and shows that the proposed approach is effective and outperforms state-of-the-art approaches

A bottleneck investigation at escalator entry at the Brisbane central train station

© 2016 ATRF, Commonwealth of Australia. All rights reserved. Escalators are an essential for passenger’s movements through multi-level rail station concourse environments. Despite the access benefits that escalators provide, they can make travel time longer and pose some challenges when bottlenecks appear at entry. Studying the passenger behaviour of bottlenecks at escalator entrances is essential for planning, designing and control of engineering transportation systems. In this paper we investigate passenger route choice behaviour while approaching an escalator-stair infrastructure set at Brisbane Central train station. A model of an escalator entry bottleneck is formulated. The developed model can explain the queuing characteristics of the bottlenecks and can be readily used to predict congested state occurrence at escalator entry bottleneck. Accurate prediction of bottlenecks occurring around escalators and the estimation of escalator capacity are obtained based on real field data collected from Brisbane Central train station. Results have provided significant insights and computational tools for understanding many features of escalator bottlenecks. Remarkably, escalator capacity at bottleneck points affects the duration and severity of the congested period

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

© 2020 The Authors. Published by Elsevier B.V. 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's 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's 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

An Algorithm for Reducing Vehicles’ Stop Behind the Bus Pre-signals

© 2019, Springer Nature Singapore Pte Ltd. One of the current controversial issues is mitigating traffic congestion in big cities. Public vehicles play a vital role in solving this matter, so it has been taken into consideration to improve not only the public transit’s infrastructure but also its functionality. More specifically, it is aimed to encourage travellers to use public vehicles. In order to serve this purpose, public transit systems’ delay needs to be decreased by prioritising them. Nowadays, pre-signal, as a modern strategy for prioritising buses, has been proposed. Pre-signal is an additional signal that is applied near the intersection to give buses priority through stopping vehicles in advance of the main intersection, and it reduces the average delay per traveller. However, it has to be mentioned that pre-signals penalise private vehicles by extra stops. This paper aims to propose a model to improve the pre-signal strategy by reducing the vehicles’ number of stops behind the pre-signals. This model would cause vehicles to be able to adjust their speed based on traffic conditions and traffic signal as well as buses’ speed and approach

Optimization of Signalized Intersections Equipped with LRV Signal Priority Systems by Minimizing Cars’ Stop Time *

There are some strategies suggested to improve the performance of intersections and increase the demand for public vehicles by providing them priority. In order to achieve this goal, several policies have been used such as Transit Signal Priority (TSP) system for Light Rail Vehicle (LRV). LRV signal priority is a timing strategy that gives priority to LRVs at signalized intersections. More specifically, this strategy is based on changing the sequence of phases, extending green time and reducing red time of LRV's phase. Although this method has considerable benefits for LRVs, it penalizes private vehicles by increasing their delay and stop time at intersections. This paper aims to propose a model to improve LRV signal priority systems. The modifying model for LRV signal priority systems minimizes the green extension and red reduction of LRV's phase by using linear programming (LP) method to calculate an optimal speed for LRVs reaching the stop line. Consequently, LRVs are prioritized while the performance of private vehicles would be improved

Performance-enhancing network pruning for crowd counting

© 2019 The Counting Convolutional Neural Network (CCNN) has been widely used for crowd counting. However, they typically end up with a complicated network model resulting in a challenge for real-time processing. Existing solutions aim to reduce the size of the network model, but unavoidably sacrifice the network accuracy. Different from existing pruning solutions, in this paper, a new pruning strategy is proposed by considering the contributions of various filters to the final result. The filters in the original CCNN model are grouped into positive, negative and irrelevant types. We prune the irrelevant filters of which feature maps contain little information, and the negative filters determined by a mask learned from the training dataset. Our solution improves the results of the counting model without fine-tuning or retraining the pruned model. We demonstrate the advantages of our proposed approach on the problem of crowd counting. Our experimental results on benchmark datasets show that the network model pruned using our approach not only reduces the network size but also improves the counting accuracies by 4% to 17% less MAE than the state of the arts