Traffic simulation in 3D world

This paper is the result of more than three months of research completed at the School of the Built Environment, Virtual Environment Suite. This study focusing on measuring the impacts of a change in the infrastructure using a 3D traffic simulation model. Various aspects about the research area and the simulation model were explained. Details on technical aspects of the simulation and the modelling are also given. The focus will lay on motor traffic and thus especially on the changes in traffic circulation as a result of relocating the car access of the City Site. The impacts of the infrastructure changes will be analysed, by simulating the current situation as well as the future situation. Comparison of the different simulations will show the impacts

Deregulating the Bus Industry.

In its Buses White Paper, the British Government sets out its proposals for abandoning quantitative control of entry to and provision of local bus services. The logic on which the proposals are based can be reduced to four propositions:- (i) Deregulation will produce a competitive market. (ii) Competition will substantially reduce costs. (iii) A competitive market will improve resource allocation. (iv) A competitive market will not cause any significant undesirable spin-off effects. Each of these propositions is suspect. If there is any competition on bus routes, it will tend to be small group rather than large group. Active rivalry involving schedule matching and price wars may occur, as may collusion. Neither will produce efficient results. Even if a competitive result were to obtain, the resulting resource allocation would not be socially efficient. A first best optimum requires subsidies because the market is subject to external economies (the Mohring effect). If Government budget constraints operate, the second-best solution then requires cross-subsidies. Competition is not compatible with social efficiency in either of these cases. Nor will the competitive market solution optimise load factors. Quality competition, in the form of minibuses 'creaming' the best traffics, may also be socially undesirable. The White Paper authors underplay the significance of these resource allocation arguments, while exaggerating the likely impact of deregulation on cost efficiency. Even though some cost savings may be available they could be obtained anyway under a regime of competitive tendering for profitable as well as unprofitable routes. Competition for the market rather than competition in the market is required

Sustainability analysis of Chinese transport policy

Whilst the world economy is developed, the life and development of human beings have been threatened by the imbalance among environmental and ecological aspects. Thus sustainability is becoming increasingly the focus of various social fields. For most developing countries, a strategy with good sustainability for social development is of long-term significance to keep the economy in expansion. This paper first reviews the conceptual framework and up-to-date development of sustainability. Second, it reviews the current transport situation of China and its future demands. Third, the paper analyses transport policy from the viewpoints of energy consumption and environment pollution caused by transportation. It finally summarises suggestions for transport policy that China should consider in the future

17-11 Evaluation of Transit Priority Treatments in Tennessee

Many big cities are progressively implementing transit friendly corridors especially in urban areas where traffic may be increasing at an alarming rate. Over the years, Transit Signal Priority (TSP) has proven to be very effective in creating transit friendly corridors with its ability to improve transit vehicle travel time, serviceability and reliability. TSP as part of Transit Oriented Development (TOD) is associated with great benefits to community liveability including less environmental impacts, reduced traffic congestions, fewer vehicular accidents and shorter travel times among others.This research have therefore analysed the impact of TSP on bus travel times, late bus recovery at bus stop level, delay (on mainline and side street) and Level of Service (LOS) at intersection level on selected corridors and intersections in Nashville Tennessee; to solve the problem of transit vehicle delay as a result of high traffic congestion in Nashville metropolitan areas. This study also developed a flow-delay model to predict delay per vehicle for a lane group under interrupted flow conditions and compared some measure of effectiveness (MOE) before and after TSP. Unconditional green extension and red truncation active priority strategies were developed via Vehicle Actuated Programming (VAP) language which was tied to VISSIM signal controller to execute priority for transit vehicles approaching the traffic signal at 75m away from the stop line. The findings from this study indicated that TSP will recover bus lateness at bus stops 25.21% to 43.1% on the average, improve bus travel time by 5.1% to 10%, increase side street delay by 15.9%, and favour other vehicles using the priority approach by 5.8% and 11.6% in travel time and delay reduction respectively. Findings also indicated that TSP may not affect LOS under low to medium traffic condition but LOS may increase under high traffic condition

Analysis of the floating car data of Turin public transportation system: first results

Global Navigation Satellite System (GNSS) sensors represent nowadays a mature technology, low-cost and efficient, to collect large spatio-temporal datasets (Geo Big Data) of vehicle movements in urban environments. Anyway, to extract the mobility information from such Floating Car Data (FCD), specific analysis methodologies are required. In this work, the first attempts to analyse the FCD of the Turin Public Transportation system are presented. Specifically, a preliminary methodology was implemented, in view of an automatic and possible real-time impedance map generation. The FCD acquired by all the vehicles of the Gruppo Torinese Trasporti (GTT) company in the month of April 2017 were thus processed to compute their velocities and a visualization approach based on Osmnx library was adopted. Furthermore, a preliminary temporal analysis was carried out, showing higher velocities in weekend days and not peak hours, as could be expected. Finally, a method to assign the velocities to the line network topology was developed and some tests carried out

ShenZhen transportation system (SZTS): a novel big data benchmark suite

Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets

Integrating big data into a sustainable mobility policy 2.0 planning support system

It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations