Bus and car travel time on urban networks : integrating Bluetooth and bus vehicle identification data

Travel time in an important transport performance indicator. Different modes of transport (buses and cars) have different mechanical and operational characteristics, resulting in significantly different travel behaviours and complexities in multimodal travel time estimation on urban networks. This paper explores the relationship between bus and car travel time on urban networks by utilising the empirical Bluetooth and Bus Vehicle Identification data from Brisbane. The technologies and issues behind the two datasets are studied. After cleaning the data to remove outliers, the relationship between not-in-service bus and car travel time and the relationship between in-service bus and car travel time are discussed. The travel time estimation models reveal that the not-in-service bus travel time are similar to the car travel time and the in-service bus travel time could be used to estimate car travel time during off-peak hours

Data requirements and graph data structures for a multi-modal, multi-objective trip planner

Traffic congestion has a significant impact on the economy and environment. Encouraging the use of multimodal transport (public transport, bicycle, park’n’ride, etc.) has been identified by traffic operators as a good strategy to tackle congestion issues and its detrimental environmental impacts. A multi-modal and multi-objective trip planner provides users with various multi-modal options optimised on objectives that they prefer (cheapest, fastest, safest, etc) and has a potential to reduce congestion on both a temporal and spatial scale. The computation of multi-modal and multi-objective trips is a complicated mathematical problem, as it must integrate and utilize a diverse range of large data sets, including both road network information and public transport schedules, as well as optimising for a number of competing objectives, where fully optimising for one objective, such as travel time, can adversely affect other objectives, such as cost. The relationship between these objectives can also be quite subjective, as their priorities will vary from user to user. This paper will first outline the various data requirements and formats that are needed for the multi-modal multi-objective trip planner to operate, including static information about the physical infrastructure within Brisbane as well as real-time and historical data to predict traffic flow on the road network and the status of public transport. It will then present information on the graph data structures representing the road and public transport networks within Brisbane that are used in the trip planner to calculate optimal routes. This will allow for an investigation into the various shortest path algorithms that have been researched over the last few decades, and provide a foundation for the construction of the Multi-modal Multi-objective Trip Planner by the development of innovative new algorithms that can operate the large diverse data sets and competing objectives.

Investigation into the noise map based on traffic flow prediction in the citywide road network

As one of the measures for decreasing road traffic noise in a city, the control of the traffic flow and the physical distribution is considered. To conduct the measure effectively, the model for predicting the traffic flow in the citywide road network is necessary. In this study, the existing model named AVENUE was used as a traffic flow prediction model. The traffic flow model was integrated with the road vehicles' sound power model and the sound propagation model, and the new road traffic noise prediction model was established. As a case study, the prediction model was applied to the road network of Tsukuba city in Japan and the noise map of the city was made. To examine the calculation accuracy of the noise map, the calculated values of the noise at the main roads were compared with the measured values. As a result, it was found that there was a possibility that the high accuracy noise map of the city could be made by using the noise prediction model developed in this study.

Information provision and network performance represented by Macroscopic Fundamental Diagram

The existence of the Macroscopic Fundamental Diagram (MFD), which relates network space-mean density and flow, has been shown in urban networks under homogeneous traffic conditions. Since the MFD represents the area-wide network traffic performances, studies on perimeter control strategies and an area traffic state estimation utilizing the MFD concept has been reported. The key requirements for the well-defined MFD is the homogeneity of the area wide traffic condition, which is not universally expected in real world. For the practical application of the MFD concept, several researchers have identified the influencing factors for network homogeneity. However, they did not explicitly take drivers’ behaviour under real time information provision into account, which has a significant impact on the shape of the MFD. This research aims to demonstrate the impact of drivers’ route choice behaviour on network performance by employing the MFD as a measurement. A microscopic simulation is chosen as an experimental platform. By changing the ratio of en-route informed drivers and pre-trip informed drivers as well as by taking different route choice parameters, various scenarios are simulated in order to investigate how drivers’ adaptation to the traffic congestion influences the network performance and the MFD shape. This study confirmed and addressed the impact of information provision on the MFD shape and highlighted the significance of the route choice parameter setting as an influencing factor in the MFD analysis.

New Perspectives On Firm Growth

This insightful volume presents a collection of innovative works by two of the leading researchers of firm growth: Per Davidsson, Director, Australian Centre for Entrepreneurship Research and Professor of Entrepreneurship, Queensland University of Technology, Australia and Jönköping International Business School, Sweden and Johan Wiklund, Professor of Entrepreneurship, Syracuse University, US and Jönköping International Business School, Sweden. The studies extend previous research by providing stronger theoretical underpinnings and using longitudinal databases that can separate in time the firms’ growth from its presumed causes. They also break new ground by examining different modes of growth, such as sales growth vs. employment growth, and organic growth vs. acquisition-based expansion. Further, the studies investigate the drivers of firm growth and take a critical look at the effects, such as under what circumstances high growth is associated with high profitability. The issue of how firm growth is achieved and managed, and what consequences it has for different stakeholders is both theoretically interesting and practically important. The book will strongly appeal to academics of entrepreneurship, small business management and strategy.

Algorithm for queue estimation with loop detector of time occupancy in off-ramps on signalized motorways

Long traffic queues on off-ramps significantly compromise the safety and throughput of motorways. Obtaining accurate queue information is crucial for countermeasure strategies. However, it is challenging to estimate traffic queues with locally installed inductive loop detectors. This paper deals with the problem of queue estimation with the interpretation of queuing dynamics and the corresponding time-occupancy distribution over motorway off-ramps. A novel algorithm for real-time queue estimation with two detectors is presented and discussed. Results derived from microscopic traffic simulation validated the effectiveness of the algorithm and revealed some of its useful features: (a) long and intermediate traffic queues could be accurately measured, (b) relatively simple detector input (i.e., time occupancy) was required, and (c) the estimation philosophy was independent with signal timing changes and provided the potential to cooperate with advanced strategies for signal control. Some issues concerning field implementation are also discussed.

Traffic queue estimation for metered motorway on-ramps through use of loop detector time occupancies

The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is a vital input for dynamic queue management on metered on-ramps. Accurate and reliable queue information enables the management of on-ramp queue in an adaptive manner to the actual traffic queue size and thus minimises the adverse impacts of queue flush while increasing the benefit of ramp metering. The proposed algorithm is developed based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. This projection results are updated with the measurement equation using the time occupancies from mid-link and link-entrance loop detectors. This study also proposes a novel single point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performances and consistently outperformed the benchmarked Single Occupancy Kalman filter (SOKF) method. The improvements over SOKF are 62% and 63% in average in terms of the estimation accuracy (MAE) and reliability (RMSE), respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in congested ramp traffic conditions where long queues may significantly compromise the benchmark algorithm’s performance.

A Kalman filter based queue estimation algorithm using time occupancies for motorway on-ramps

The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is the most vital input for a dynamic queue management that can treat long queues on metered on-ramps more sophistically. The proposed algorithm is developed based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. This projection results are updated with the measurement equation using the time occupancies from mid-link and link-entrance loop detectors. This study also proposes a novel single point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performances and consistently outperformed the benchmarked Single Occupancy Kalman filter (SOKF) method. The improvements over SOKF are 62% and 63% in average in terms of the estimation accuracy (MAE) and reliability (RMSE), respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in the congested ramp traffic conditions where long queues may significantly compromise the benchmark algorithm’s performance.