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• Context-specific built environment and transit travel behaviour of urban villages are measured in each village unit. • Total, direct and indirect effects of built environment on transit travel are revealed. • Residents in urban villages use less public transit when residential density increases. • Mixed land use has no significant effect on transit travel behavior. Context-specific research are necessary to promote public transit by optimizing the built environment in the process of urban renewal. Using data of residential travel survey in Shenzhen, China in 2014, this paper investigated how built environment affected transit travel in urban villages where low-income group resided, and they are all tenants. Structural equations modelling was employed to express the effects of socio-economic, public transit service related and built environment variables on travel mode choice, travel time and distance, with control for residential self-selection. It is found that after considering the self-selection effect, the influence of density and transit availability on travel distance, travel time and transit mode choice in urban villages are still significant. For urban villages, transit availability matters greatest for transit promotion, but the negative effect of density on transit mode choice and none effect of mixed land use are unusual as previous findings. This is partly because of relatively low income, low car ownership, and less elastic travel demand of tenants living in urban villages. Our findings provide some insights into transit-oriented urban renewal that when transforming urban villages, emphasis should be put on enhancement of transit availability, and the mixed land use could be put in the last consideration when there is limited time and funds. [ABSTRACT FROM AUTHOR]

Nuclear power has incomparable advantages over traditional fossil energy and has become one of major power sources. Meanwhile, the huge losses in nuclear accidents place higher requirements for emergency response. Nuclear emergency evacuation is an effective protective measure to prevent or reduce the public's exposure to radioactive materials. However, nuclear emergency evacuation is affected by a variety of factors, including environment, resources, transportation, and radiation, etc. It is difficult for decision makers to analyze a large amount of complex information so as to design effective nuclear emergency evacuation schemes. Considering the potential risk and characteristics of emergency traffic evacuation, a partition evacuation framework based on risk assessment is proposed in this paper. First, to deal with nuclear radiation and traffic evacuation vulnerability, a comprehensive risk assessment method is put forward that can be used for potential risk quantification during nuclear emergency evacuation. Second, according to the risk assessment results, this paper proposes a partition evacuation organization strategy and a cell transmission model (CTM) to minimize the exposure risk of during nuclear emergency evacuation. Finally, taking a coastal nuclear power plant in China as the research object, extensive and in-depth analysis is performed. As suggested by the simulation results, the partition evacuation strategy we proposed could effectively reduce the exposure risk by 36.0% and the average delay is shortened by 50.4%. Compared with traditional evacuation strategies, our framework is useful for increasing the nuclear emergency evacuation efficiency and alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

One function of road lighting is to enable pedestrians to evaluate the intentions of other people or to make interpersonal judgement. This paper aims to investigate the effect of lighting conditions on the ability of pedestrian to recognize facial expressions of 3D models. Fifteen lighting conditions were examined: five vertical illuminances (0.33, 1.00, 3.33, 10.0, and 30.0 lx) crossing spectral power distribution with three different scotopic/photopic ratios (0.57, 1.77 and 25.13). The results show that recognition of 3D facial expressions tended to be improved as illuminance increased. However, manipulation on spectral power distribution would not lead to better performance on 3D facial expression recognition. Compared to previous studies that used 2D images as visual stimuli, the current results from the 3D model present a slightly better performance on facial expression recognition. These findings are expected to contribute to the recommendations of optimal lighting levels based on facial expression recognition, and provide further evidence for the development of lighting standards. • Effect of pedestrian lighting on facial expression recognition with 3D models was investigated. • Fifteen lighting conditions were examined: five vertical illuminances crossing three spectral power distributions. • The 3D model can facilitate facial expression recognition than 2D photographs. • Manipulation on spectral power distribution would not lead to better performance on 3D facial expression recognition. [ABSTRACT FROM AUTHOR]

In this paper, we propose a face recognition algorithm by incorporating a neighbor matrix into the objective function of sparse coding. We first calculate the neighbor matrix between the test sample and each training sample by using the revised reconstruction error of each class. Specifically, the revised reconstruction error ( RRE ) of each class is the division of the l 2 -norm of reconstruction error to the l 2 -norm of reconstruction coefficients, which can be used to increase the discrimination information for classification. Then we use the neighbor matrix and all the training samples to linearly represent the test sample. Thus, our algorithm can preserve locality and similarity information of sparse coding. The experimental results show that our algorithm achieves better performance than four previous algorithms on three face databases. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images. [Copyright &y& Elsevier]

Last mile transportation is important in both freight and passenger transport as it accounts for a large portion of the costs and emissions in the transportation industry. In urban transport, the continuously growing travel demands and the rapid development of mass transit systems place a high stress on last mile transportation, which is a vital but underdeveloped part of urban transportation systems. This underdevelopment greatly impedes the further improvement of bus sharing rates and the realisation of sustainable transportation. Therefore, this research proposes a data-driven method to design shuttle services to improve the efficiency and convenience of last mile transportation. Specifically, a unified tool is developed to identify the last mile travel demands from various data sources. Based on these demands, the locations of bus stop are planned through an improved clustering algorithm, and the routing and scheduling of shuttle services are designed using a data-driven method. In addition, a simulation-based cost-benefit analysis is conducted to evaluate the performances of shuttle services in different areas. Finally, a case study using bicycle-sharing data in Shanghai is presented to demonstrate the working process of the proposed method and verify its performance. [ABSTRACT FROM AUTHOR]

Bus travel time often fluctuates greatly due to disturbance factors such as traffic conditions and passenger flow, which causes a reduction in the transit service as it cannot be provided as planned. Bus dispatching is necessary to handle this problem. Two crucial factors for real-time bus dispatching have been analysed in this study namely the bus arrival time prediction and timetable redesign. First, a bus arrival time prediction model combining Support Vector Regression (SVR) and Kalman Filter (K-SVR) was proposed. The headway is selected as an intermediate variable to improve the prediction accuracy by limiting the disturbance factors between two adjacent bus stops. Besides, by adjusting the parameters of the Kalman Filter, the predictive results are more suitable for practice to avoid the frequency of unnecessary timetable adjustment. Furthermore, an automatic timetable redesign method is given based on the proposed circle search algorithm, which can minimise the impact on the initial schedule by finding the minimum adjustment range in consideration of the normal working order. A case study in Shenzhen, China was conducted and the results verify that the K-SVR method could improve the prediction accuracy especially in peak hours. On this basis, in comparison with the traditional method, the timetable redesign algorithm could allocate the transit resources appropriately and perform well on indicators such as the headway fluctuation, compliance rate of headways, and schedule completeness. Moreover, it is more operable and valuable as a result of sending the departure instructions appropriately and maintaining the normal working order of drivers realistically. [ABSTRACT FROM AUTHOR]