Subjective well-being of Beijing taxi drivers

This study investigates subjective well-being among a sample of Beijing taxi drivers in the lead up to the 2008 Beijing Olympic Games using the Personal Wellbeing Index (PWI). The specific aims of this study are (a) investigate the psychometric properties of the PWI in this unique population; (b) ascertain whether Beijing taxi drivers are satisfied with their lives; and (c) examine whether the responses to the PWI from participants falls within the narrow range predicted by the 'Theory of Subjective Wellbeing Homeostasis'. The PWI demonstrated good psychometric properties and was consistent with previous studies for Western and non-Western samples. The data revealed a moderate level of subjective well-being (PWI score = 61.1). While Beijing taxi drivers work long hours for low wages, the PWI was nonetheless within the normative range predicted for Chinese societies by the 'Theory of Subjective Wellbeing Homeostasis'. The results suggest that the homeostatic mechanism is fairly resilient, even when the individual leads relatively a hard life based on objective indicators. Specifically, for Beijing taxi drivers, it appears that external, buffers such as personal relationships and feeling part of the community, act to assist the homeostatic system. © 2009 Springer Science+Business Media B.V.

A C-DBSCAN algorithm for determining bus-stop locations based on taxi GPS data

Determining suitable bus-stop locations is critical in improving the quality of bus services. Previous studies on selecting bus stop locations mainly consider environmental factors such as population density and traffic conditions, seldom of them consider the travel patterns of people, which is a key factor for determining bus-stop locations. In order to draw people’s travel patterns, this paper improves the density-based spatial clustering of applications with noise (DBSCAN) algorithm to find hot pick-up and drop-off locations based on taxi GPS data. The discovered density-based hot locations could be regarded as the candidate for bus-stop locations. This paper further utilizes the improved DBSCAN algorithm, namely as C-DBSCAN in this paper, to discover candidate bus-stop locations to Capital International Airport in Beijing based on taxi GPS data in November 2012. Finally, this paper discusses the effects of key parameters in C-DBSCAN algorithm on the clustering results. Keywords Bus-stop locations, Public transport service, Taxi GPS data, Centralize density-based spatial clustering of applications with noise.

Cracking it open

Cracking it Open puts the spotlight on improvisation with live performances by some of Melbourne's leading improvisation artists. Each artist presents a short performance, and then opens the floor for discussion with audience members.

Occupational light vehicle use: characterising the at-risk population

Previous occupational light vehicle research has concentrated on employees using cars. The aim of this study was to identify and characterise the total occupational light vehicle-user population and compare it with the privately-used light vehicle population. Occupational light vehicle and private light vehicle populations were identified through use-related 2003 registration categories from New South Wales Roads and Traffic Authority data. Key groups of occupational light vehicle registration variables were comparatively assessed as potential determinants of occupational light vehicleuser risks. These comparisons were expressed as odds ratios with 95% Confidence Intervals. The occupational light vehicle population vehicles (n=646,201) comprised 18% of all light vehicle registrations. A number of statistical differences emerge between the two populations. For instance, 86% of occupational light vehicle registrants were male versus 65% of private registrants, and 56% of the occupational users registered load shape vehicles versus 20% of the private registrants. Occupational light vehicles registered for farming or taxi use were more than six times more likely to belong to sole-traders than organisations. Sole-traders were nearly twice as likely to register light-trucks, and twice as likely to register older vehicles, than organisations. This study demonstrates that the occupational light vehicle user population is larger and more diverse than previously shown with characteristics likely to increase the relative risks of motor vehicle crashes. More occupational light vehicles were load shapes and therefore likely to have poorer crashworthiness ratings than cars. Occupational light vehicles are frequently used by sole-traders for activities with increased OHS risks including farming and taxi use. Further exploration of occupational light vehicle-user crash risks should include all vehicle types, work arrangements and small ‘fleets’.

Dividing traffic sub-areas based on a parallel K-means algorithm

In order to alleviate the traffic congestion and reduce the complexity of traffic control and management, it is necessary to exploit traffic sub-areas division which should be effective in planing traffic. Some researchers applied the K-Means algorithm to divide traffic sub-areas on the taxi trajectories. However, the traditional K-Means algorithms faced difficulties in processing large-scale Global Position System(GPS) trajectories of taxicabs with the restrictions of memory, I/O, computing performance. This paper proposes a Parallel Traffic Sub-Areas Division(PTSD) method which consists of two stages, on the basis of the Parallel K-Means(PKM) algorithm. During the first stage, we develop a process to cluster traffic sub-areas based on the PKM algorithm. Then, the second stage, we identify boundary of traffic sub-areas on the base of cluster result. According to this method, we divide traffic sub-areas of Beijing on the real-word (GPS) trajectories of taxicabs. The experiment and discussion show that the method is effective in dividing traffic sub-areas.

An efficient mapreduce-based parallel clustering algorithm for distributed traffic subarea division

Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs). Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K -Means (Par3PKM) algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K -Means and then employ a MapReduce paradigm to redesign the optimized K -Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K -Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

Complex network theoretical analysis on information dissemination over vehicular networks

How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.