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A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple factors and by considering the different user preferences regarding these factors. More comprehensive methodologies to assess urban bicycle networks are essential to the operation and planning of modern city transportation. This work proposes a multi-objective methodology to assess—what we refer to as—bikeability between origin–destination locations and over the entire network, useful for evaluation and planning of bicycle networks. We do so by introducing the concept of bikeability curves which allows us to assess the quality of cycling in a city network with respect to the heterogeneity of user preferences. The application of the proposed methodology is demonstrated on two cities with different bike cultures: Amsterdam and Melbourne. Our results suggest the effectiveness of bikeability curves in describing the characteristic features and differences in the two networks. [ABSTRACT FROM AUTHOR]

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method. [ABSTRACT FROM AUTHOR]

Background and purpose: Two hubs are designated to provide endovascular clot retrieval (ECR) for the State of Victoria, Australia. In an earlier study, Google Maps application programming interface (API) was used to perform modeling on the combination of hospitals optimizing for catchment in terms of current traveling time and road conditions. It is not known if these findings would remain the same if the modeling was performed with a large-scale transport demand model such as Victorian Integrated Transport Model (VITM). This model is developed by the Victorian State Government Transport has the capability to forecast travel demand into the future including future road conditions which is not possible with a Google Maps based applications. The aim of this study is to compare the travel time to potential ECR hubs using both VITM and the Google Maps API and model stability in the next 5 and 10 years. Methods: The VITM was used to generate travel time from randomly generated addresses to four existing ECR capable hubs in Melbourne city, Australia (i.e., Royal Melbourne Hospital/RMH, Monash Medical Center/MMC, Alfred Hospital/ALF, and Austin Hospital/AUS) and the optimal service boundaries given a delivering time threshold are then determined. Results: The strategic transport model and Google map methods were similar with the R 2 of 0.86 (peak and off peak) and the Nash-Sutcliffe model of efficiency being 0.83 (peak) and 0.76 (off-peak travel). Futures modeling using VITM found that this proportion decreases to 82% after 5 years and 80% after 10 years. The combination of RMH and ALF provides coverage for 74% of cases, 68% by 5 years, and 66% by 10 years. The combination of RMH and AUS provides coverage for 70% of cases in the base case, 65% at 5 years, and 63% by 10 years. Discussion: The results from strategic transport model are similar to those from Google Maps. In this paper we illustrate how this method can be applied in designing and forecast stroke service model in different cities in Australia and around the world. [ABSTRACT FROM AUTHOR]

• A general framework for estimating origin–destination matrix from smartcard data. • The proposed framework does not require expert knowledge on the system under study. • Using statistical pattern recognition, passengers' behavior is learned from the data. • Parameters which used to be determined manually are inferred analytically from the data. • Patterns identified in the public transport network are consistent with actuality. Origin-destination travel demand matrix is the signature of travel dynamics in transportation networks. Many fundamental analyses of transportation systems rely on the origin–destination demand matrix of the network. Although extraction of origin–destination travel demand for public transportation networks from ticketing data is not a new problem, yet it entails challenges, such as 'alighting transaction inference' and 'transfer identification' which are worthy of further attention. This is mainly because the state-of-the-art solutions to these challenges, are often heavily reliant on network-specific expert knowledge and extensive parameter setting, or multiple data sources. In this paper, we propose a procedure that effectively applies statistical pattern recognition techniques to address the main challenges in extracting the origin–destination demand from passenger smartcard records. Learning from patterns in the available data allows the procedure to perform well under minimum case-specific assumptions, thus it becomes applicable to smartcard data from various public transportation systems. The performance of the proposed framework is tested on a dataset of over 100 million smartcard transaction records from Melbourne's multi-modal public transportation network. Evaluations on different aspects of the proposed procedure, suggest that the identified tasks are well addressed, and the framework is able to extract an accurate estimation of the origin–destination demand matrix for the system. [ABSTRACT FROM AUTHOR]

• Propose techniques to make the IMM filtering approach computationally attractive by reducing the complexity from exponential to polynomial under incident-free conditions. • Develop a method to mathematically derive the cross-covariance of process and measurement noises and calibrate using real measured data. • Show how the novel noise modeling technique leads to unconventional dynamic equations containing gain matrices with random variables as entries. • Develop method to derive filtering equations from unconventional dynamic equations applying first principles thereby obtaining the Stochastic Kalman Filter. Traffic state estimation plays an important role in Intelligent Transportation Systems (ITS). It provides the latest traffic information to travelers and feedback to signal control systems. The Interactive Multiple Model (IMM) filtering provides a powerful estimation method to deal with the non-differentiable nonlinearity caused by the phase transitions between the under-critical and above-critical traffic density regimes. The IMM filtering also accounts for the uncertainty in the current 'mode of operation'. In this paper, we develop an enhanced IMM filtering approach to traffic state estimation, with an underlying Cell Transmission Model (CTM) for traffic flow propagation. We improve the IMM filtering with CTM in two ways: (1) We apply two simplifying assumptions that are highly likely to hold in urban roads in incident-free conditions, which makes the computational complexity to grow with the number of cells only polynomially , rather than exponentially as reported in prior work. (2) We apply a novel approach to noise modeling wherein the process noise is explicitly obtained in terms of the randomness in more fundamental quantities (e.g., free-flow speed, maximum flow capacity, etc.), which not only makes noise calibration using real data convenient but also makes the computation of the cross-correlation between the process and measurement noises transparent. However, it leads to 'process dynamic' and 'measurement' equations that involve multiplier matrices whose elements are random variables rather than deterministic scalars, and hence, standard filtering equations cannot be applied. We derive the appropriate filtering equations from first principles. We calibrate the traffic parameters and the total inflow and outflow on the links using the SCATS loop detector data collected in Melbourne and report significant improvements in accuracy, which is due to the accurate computation of the cross-covariance of process and measurement noises. [ABSTRACT FROM AUTHOR]

• Address equity issues from the perspective of travel perception difference in congestion pricing. • Combine the concept of level of service (LOS) and demand management into a single pricing scheme. • The perceived LOS is quantified by cumulative prospect values (CPV) to design the new pricing scheme. • The proposed pricing yields positive savings with total time savings outweigh revenues collected. This paper proposes a novel congestion pricing scheme for the metropolitan urban networks where a cumulative prospect theory (CPT) is adopted to quantify the travelers' degree of satisfaction during their trips. Using the concept of the cumulative prospect values (CPV) in CPT, an area-based congestion price (or toll) is designed to reduce the peak-hour travel demand while taking into account the level of service perceived by the travelers. We demonstrate the effectiveness and efficiency of the proposed pricing scheme via an agent-based simulation for the Melbourne network. Results of the proposed CPV-based distance toll is compared with the flat distance-based toll and bi-direction cordon toll in terms of the network performance, behavioral changes, and social welfare improvement. Promisingly, the proposed CPV-based pricing scheme can reduce the long-distance trips (greater than10 km) inside the congested central business district by over 30% while discourage non-commuting trips during peak hours, making more road resources available to commuting trips. Furthermore, it is found that the proposed CPV-based pricing scheme yields positive savings, where the total time savings outweigh the revenues collected, thus proving its high efficiency and desirability among all the comparative pricing schemes. [ABSTRACT FROM AUTHOR]