In this paper we assess the accuracy with which General Transit Feed Specification (GTFS) schedule data can be used to measure accessibility by public transit as it varies over space and time. We use archived Automatic Vehicle Location (AVL) data from four North American transit agencies to produce a detailed reconstruction of actual transit vehicle movements over the course of five days in a format that allows for travel time estimation directly comparable to schedule-based GTFS. With travel times estimated on both schedule-based and retrospective networks, we compute and compare a variety of accessibility measures. We find that origin-based accessibility even when averaged over one-hour periods can vary widely between locations. Origins with lower scheduled access tend to produce less reliable estimates with more variability from hour to hour in real accessibility, while higher access zones seem to converge on an estimate 5-15\% lower than the schedule predicts. Such over- and under-predictions exhibit strong spatial patterns which should be of concern to those using accessibility metrics in statistical models. Momentary measures of accessibility are briefly discussed and found to be weakly related to momentary changes in real access. These findings bring into question the validity of some recent applications of GTFS data and point the way toward more robust methods for calculating accessibility.

On the Accuracy of Schedule-Based GTFS for Measuring Accessibility

A novel origin destination based transit supply index: Exploiting the opportunities with big transit data

Comparative analysis of multimodal travel demand can help transport planners to improve the sustainability of a transport system. This research proposes a framework to qualitatively compare and analyze multimodal travel demand (of constrained and free transport modes) to identify opportunities and prioritize areas for improvement of constrained mode usage. The framework includes two methods. First method, CLAN is a Coarser Level ANalysis, comparing travel patterns and demand ratios of the two modes. Second method, FLAN is a Finer Level ANalysis based on density distributions of zones and OD pairs. The proposed framework is applied to the car (relatively free mode) and transit (constrained mode) OD matrices developed from observed Bluetooth and smart card data, respectively, for the Brisbane City Council region, Australia. The gaps in transit service usage are identified at different sections of the network using both methods. The findings from this study reveal that CLAN can be effectively used to prioritize OD pairs for transit improvement at a coarser level. The OD pairs with the highest and least priority from CLAN are further tested using FLAN. The results from FLAN further confirmed the findings from CLAN. The study showed that both methods have their own advantages and disadvantages if applied independently. However, when applied together, the two methods can help prioritize zones and OD pairs for wider benefits of transport systems such as improvement in transit patronage. The methodological framework proposed in this study is generic and can be applied to compare other multimodal combinations.

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A Framework for the Comparative Analysis of Multi-Modal Travel Demand: Case Study on Brisbane Network

Increased travel times are often observed on urban roads, with signalized intersections being the major bottlenecks. The inability of existing static signal timings in accommodating the actual demand fluctuations could be one of the contributing factors. A traffic-responsive signal control system that changes signal timings according to traffic volume fluctuations may alleviate this problem. However, such problems are conventionally formulated based on the data collected from location-based sensors, which are infrastructure intensive and costly and fail to capture mixed and disordered traffic conditions. Considering these limitations, this paper presents an optimal signal design using sample travel time information collected from mobile data sources such as GPS/Bluetooth/Wi-Fi sensors that work independently of the traffic conditions and are relatively cost-effective. The proposed adaptive signal design minimizes total intersection delay at isolated intersections for every cycle based on the traffic conditions observed in the previous cycle. The mathematical programming-based formulation uses shock waves formed during the red and green phases to estimate optimal-phase durations. Results revealed that the proposed design is capable of handling traffic flow fluctuations without requiring the entire traffic stream data. The system demonstrated that sample data from four probe vehicles per phase is adequate for real-time optimal signal design. Results showed that the proposed model outperformed the existing Webster’s signal design procedure with a delay reduction of 11.78% when compared theoretically and 10.41% when implemented in VISSIM.

Optimal Signal Control Design for Isolated Intersections Using Sample Travel-Time Data

Travel time is one of the elementary traffic stream parameters in both users’ and transport planners’ perspective. Conventional travel time estimation methods have performed out of sorts for Indian urban traffic conditions characterized by heterogeneity in transport modes and lack of lane discipline. Robust to these limitations, Media Access Control (MAC) matching is perceived to be a reliable alternative for travel time estimation. To assist with real-time traffic control strategies, this study aims at developing a reliable structure for forecasting travel time on Indian urban arterials using data from Wi-Fi/ Bluetooth sensors. The data collected on an urban arterial in Chennai has been used as a case study to explain the value of such data and to explore its applicability in implementing various prediction models. To this end, this study examines and compares three different machine learning algorithms k-Nearest Neighbour (kNN), Random Forest (RDF), Naive Bayes, and Kalman filtering technique for prediction. The performance of each model is evaluated to understand its suitability.

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Comparative analysis of travel time prediction algorithms for urban arterials using Wi-Fi Sensor Data

Efficient planning, operation and management of transportation facilities require extensive data regarding the traffic demand, patterns and conditions prevalent in the transportation network. Conventional data collection techniques such as loop detectors bear practical limitations such as limited accuracy and applicability, especially in mixed traffic conditions. Since use of smart phones has gained prominence in the recent times, crowd sourced data using Bluetooth and Wi-Fi technologies is perceived to be a reliable alternative for traffic data collection. This eases the rigorous data collection process by considerably reducing the investments on labor, time and other resources. Significant research has been carried out in the extraction and analysis of traffic data from Bluetooth sensors. Changes in privacy settings of smartphones has necessitated the devices to be put on “Discoverable” mode for passive data collection thereby resulting in drastic drops in market penetration rates. Unlike their Bluetooth counterparts, Wi-Fi protocol just requires the Wi-Fi to be switched on for passive data collection, thus resulting in higher penetration rates. This paper presents a preliminary analysis of data extracted from Wi-Fi sensors and the use of it for extracting the signal state information.

Analysis and Use of Wi-Fi data for Signal State Identification

This paper presents an open source framework with key logic units design to assist with the acquisition, elaboration, storage and visualization of General Transit Feed Specification (GTFS) real-time data. It bridges the gap for researchers and planners wanting access to GTFS real time through providing the resources to understand and begin extracting GTFS data. To showcase the applicability, the framework is applied on the real data from Translink, Brisbane, Australia and the extracted data is utilised for performance measures and dashboard visualization. The codes are open sourced and are available at https://github.com/darronlim/GTFS_LiveFeed_Extraction.

Shubham Sharma

Papers

Analysis and Use of Wi-Fi data for Signal State Identification

An open source framework for GTFS data analytics: Case study using the Brisbane TransLink network

Comparative analysis of travel time prediction algorithms for urban arterials using Wi-Fi Sensor Data