Advancements in Automatic Vehicle Location (AVL) Technology

In this session we discuss the City of Fishers Department of Public Works’s cutting-edge AVL system, which utilizes the ESRI GIS platform and allows the City to deploy its fleet based on live data, including location, speed, history of routes driven, and idle time, saving the City thousands of dollars. Data are displayed in each truck, and a web-based map allows management to reallocate resources during a winter storm

Application of automatic vehicle location in law enforcement: An introductory planning guide

A set of planning guidelines for the application of automatic vehicle location (AVL) to law enforcement is presented. Some essential characteristics and applications of AVL are outlined; systems in the operational or planning phases are discussed. Requirements analysis, system concept design, implementation planning, and performance and cost modeling are described and demonstrated with numerous examples. A detailed description of a typical law enforcement AVL system, and a list of vendor sources are given in appendixes

Location privacy acceptance: attitudes to transport-based location-aware mobile applications on a university campus

Location-based services feature in many information systems but attitudes to location privacy and their impact on transport app usage are less common. This paper builds on a use-case, the implementation of UniShuttle, a smartphone transport app developed by the authors, that provides users with real-time bus location and arrival information from an Automatic Vehicle Location (AVL) system. In return, the AVL system tracks and warehouses user interactions with the transport network. The paper describes a pre- and post-implementation survey of user attitudes toward location privacy, and how some app features of the transport app trade-off against privacy concerns

Vehicle Delay-Driven Passenger Delay Modelling: An Agent-Based Copenhagen Case Study

Travel time of passengers is often uncertain due to lack of punctuality of public transport services. Whereas Automatic Vehicle Location (AVL) data makes it easy to measure the punctuality of public transport vehicles themselves, calculating door-to-door passenger punctuality is challenging, as both the intended and realised routes of passengers have to be considered. This study introduces a MATSim mesoscopic simulation framework for evaluating passenger punctuality caused by vehicle delays in the railway system in the metropolitan area of Copenhagen. Based upon empirical train delay data for 65 weekdays in the autumn of 2014, the model shows that passenger punctuality is considerably smaller than train punctuality, with 17.8% of the passengers using the railway system being delayed more than a minute compared to their intended plan

Proactive Travel Time Predictions Under Interrupted Flow Condition

This research is focused on the development of a model for estimating arterial travel time by utilizing an automatic vehicle location (AVL) system-equipped bus as a probe vehicle. As an initial achievement, a prototype arterial travel time estimation model, applied to the bus arrival time estimation, was developed. The methodology adopted in this phase of the travel time estimation model was the online parameter adaptation algorithm. Three objectives were identified for this phase of the research. These were: (1) studying dynamics of bus behavior at a single bus stop; (2) extending the dynamics of bus behavior study to multiple bus stops; and (3) developing a prototype bus arrival time prediction model. The prototype travel time estimation was tested and evaluated through the simulation

Using Automatic Vehicle Location (AVL) for real-time maintenance identification and tracking

Improving the communication of work zone data has been the main focal point of the Work Zone Data Exchange (WZDx), which provides a standard protocol to push agency data to third party users. However, the accuracy of the work zone locations and times are difficult for many agencies to collect and maintain. Many agencies are beginning to focus on improving this data through the use of connected temporary traffic control devices such as smart arrow boards. Maintenance operations are another area that must be improved because of the short duration and the dynamic nature of the work. Currently, most maintenance operations must notify a traffic management center (TMC)of their work, but this may extend multiple miles of roadway they are working on that day, and if the work is cut short or ends early, the TMC is not always notified. To improve this data, many agencies currently equip their vehicles with AVL systems to track their snowplows. The snowplows, in most cases, also are the same vehicles used for maintenance operations such as painting, pothole repairs, etc. To minimize the work by the maintenance staff and improve the quality of work zone data, the AVL data points can be used to classify a vehicle in maintenance mode then cluster all of the surrounding vehicles to get the extents of the maintenance operation. As the vehicles move, the data will be updated in real-time to accurately communicate the extents of the maintenance operation through the agencies WZDx or ATIS system. This thesis develops an automated process that identifies maintenance activities and clusters AVL data to communicate actual maintenance operations in real-time through the WZDx

bus travel time variability some experimental evidences

Abstract Bus travel time analysis is essential for transit operation planning. Then, this topic obtained large attention in transport engineering literature and several methods have been proposed for investigating its variability. Nowadays, the availability of large data quantities through automated monitoring allows more in-depth this phenomenon to be pointed out with new experimental evidence. The paper presents the results of some analyses carried out using automatic vehicle location (AVL) data of bus lines and automated vehicle counter (AVC) data on some corridors in the urban area of Rome where the bus services are mixed with other traffic and travel times are subject to high degrees of variability. The results show the effect of temporal dimension and similarity between travel time and traffic temporal patterns, and could open the road for the improvement of the short-term forecasting methods, too

Punctuality Predictions in Public Transportation: Quantifying the Effect of External Factors

Increasing availability of large-scale datasets for automatic vehicle location (AVL) in public transportation (PT) encouraged researchers to investigate data-driven punctuality prediction models (PPMs). PPMs promise to accelerate the mobility transition through more accurate prediction delays, increased customer service levels, and more efficient and forward-looking planning by mobility providers. While several PPMs show promising results for buses and long-distance trains, a comprehensive study on external factors\u27 effect on tram services is missing. Therefore, we implement four machine learning (ML) models to predict departure delays and elaborate on the performance increase by adding real-world weather and holiday data for three consecutive years. For our best model (XGBoost) the average MAE performance increased by 17.33 % compared to the average model performance when only trained on AVL data enriched by timetable characteristics. The results provide strong evidence that adding information-bearing features improves the forecast quality of PPMs

A descriptive study on public transport user behaviour from Live Bus Arrivals

In order to offer public transport that meet citizens’ needs for transport and further increase the use of bus services, Public Authorities need to analyse and understand travellers behaviour. Automatic Vehicle Location (AVL) data provide information on the observed time of arrival and departure of a bus at each stop. These data are fed into an algorithm to provide information to users on the expected time of arrival at the bus stop by an on-line service. In the city of London this service is called Live Bus Arrivals. This work describes the general behaviour of Live Bus Arrivals users by analysing the type of requests, localising them and compare them in different days of the week and time ranges. The objective is to identify some of the main passengers’ origin, destination and interchanges behaviour that could be of value to decision-makers and planners