Advanced traveller information system (ATIS) using GPS/GIS

Vehicle arrival time is one of the most important factors of intelligent transportation systems (ITS). Accurate transit travel information is important because it attracts additional customers and increases the satisfaction of transit users. A passenger waiting for a train or bus, a person waiting for a cab, a customer waiting for a courier to come to his/her home to pickup or deliver a package, a business office waiting for a truck for goods and a home user waiting for his/her shipment for which he/she did online shopping are a few examples of how important vehicle arrival time is in different areas of life. Most companies are investing a lot of money to improve their systems for better, faster and reliable customer service. As the cost of ITS components have decreased, the automatic vehicle location (AVL) system, which is one component of ITS, has become more widely used. Many transit agencies use an AVL system to track their vehicles in real-time. Tracking systems technology was made possible by the integration of three technologies: global positioning system (GPS), global system for mobile communication (GSM) and the geographic information system (GIS). This project shows detailed research in the area of automatic vehicle location and implements a low cost vehicle tracking system using GPS and GPRS. The system reads the current position, speed and direction using GPS, the data is sent via GPRS service from a GSM network to a server using TCP/IP protocol and the server saves this information to the database on a regular time interval. The web-based application then uses this data and calculates the approximate arrival time. The system allows a user to view the present position of the vehicle using Google Maps and calculates the arrival time. Also, bus location can be monitored in real time by route supervisors. This will allow supervisors to make better service adjustment decisions because they will be able to see how the route is operating. The test bed was a bus route running in the downtown of Toronto.

Advanced traveller information system (ATIS) using GPS/GIS

Vehicle arrival time is one of the most important factors of intelligent transportation systems (ITS). Accurate transit travel information is important because it attracts additional customers and increases the satisfaction of transit users. A passenger waiting for a train or bus, a person waiting for a cab, a customer waiting for a courier to come to his/her home to pickup or deliver a package, a business office waiting for a truck for goods and a home user waiting for his/her shipment for which he/she did online shopping are a few examples of how important vehicle arrival time is in different areas of life. Most companies are investing a lot of money to improve their systems for better, faster and reliable customer service. As the cost of ITS components have decreased, the automatic vehicle location (AVL) system, which is one component of ITS, has become more widely used. Many transit agencies use an AVL system to track their vehicles in real-time. Tracking systems technology was made possible by the integration of three technologies: global positioning system (GPS), global system for mobile communication (GSM) and the geographic information system (GIS). This project shows detailed research in the area of automatic vehicle location and implements a low cost vehicle tracking system using GPS and GPRS. The system reads the current position, speed and direction using GPS, the data is sent via GPRS service from a GSM network to a server using TCP/IP protocol and the server saves this information to the database on a regular time interval. The web-based application then uses this data and calculates the approximate arrival time. The system allows a user to view the present position of the vehicle using Google Maps and calculates the arrival time. Also, bus location can be monitored in real time by route supervisors. This will allow supervisors to make better service adjustment decisions because they will be able to see how the route is operating. The test bed was a bus route running in the downtown of Toronto.

En-route headway-based bus reliability with real-time data at network scale

Bus reliability has long attracted attention and been extensively studied to enhance service quality. However, existing research generally evaluates bus reliability of specific routes or stops. To this end, this study explores en-route bus reliability with real-time data at network scale. Drawing on data of bus automatic vehicle location and smart card usage in Ningbo, China, this study calculates headway-based reliability with the difference between actual and scheduled headway at each stop. To demonstrate the trend of stop-level reliability along a bus route, reliability is graded and visualized on a map with ridership at each stop, which is then weighted with passenger-boarding volume. Route-level reliability is then quantified and mapped, where unreliable service basically concentrates in or extends through the centre area. With respect to network-level reliability, temporal changes are demonstrated with ridership on weekdays and at the weekend. It is observed that on weekdays, the reliability trend is similar to that of ridership, implying a causal relationship between bus travel-time variation and bus waiting-time at stops. Furthermore, a reliability comparison between weekdays in December and October shows the necessity of evaluating periodically and around important events to avoid negative riding experiences that discourage public transport usage. This research provides insights for bus agencies to systematically evaluate service reliability both spatially and temporarily, in order to identify and prioritize the routes and stops where the scope for reliability improvement and the expected benefit are greatest.

Characteristics Analysis of Bus Stop Failure Using Automatic Vehicle Location Data

Queue forming behind a bus stop on an urban street is common and a traffic bottleneck usually occurs around the bus stop area. The bus stop failure means arriving buses cannot move into the bus stop due to limited capacity but have to wait for available loading areas. It is related with the transit operation level. Traditionally, the failure rate (FR), defined as the percentage of buses that arrives at the bus stop to find all loading areas occupied, is adopted in bus capacity analysis. However, the concept of FR is unable to quantitatively analyze failure characteristics in terms of its dispersion and uncertainty over time. Therefore, in this paper, we propose a new index called failure duration rate (FDR) to evaluate the bus stop failure, which can characterize waiting time for traffic delay calculation and capacity drop estimation. The automatic vehicle location data at eight bus stops in Wujiang District Suzhou, China, over 56 working days, are used to analyze the temporal characteristics of FR and FDR. We next examined the failed service duration characteristics during peak hours at the eight bus stops. Based on these characteristics analyses, we then proposed a Distribution Fitting and Cumulative Distribution Correlation (DF-CDC) approach to explore the correlation between FDR and FR at the same cumulative distribution function levels and validated the bus stop failure performance using the cross-validation method. The analysis results revealed that (i) FR fluctuates more significant than FDR, (ii) FDR is a more robust index than FR in describing the traffic characteristics incurred by bus stop failures, and (iii) FDR performs better in failure characteristics analysis than FR.