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In cities, road traffic accidents are critical endangerment to people’s safety. A vast number of studies which are designed to understand these accidents’ leading causes and mechanisms exist. The widely held view is that emerging analysis methods can be a critical tool for understanding the complex interactions between land use and urban transportation. Using a case study of Suzhou Industrial Park (SIP) in Suzhou, China, this paper examines the relationship between different land use types and traffic accidents using a gradient boosting model (GBM) machine learning method. The results show that the GBM can be used as an effective accident model for a variety of research and analysis methods by (1) ranking the influential factors, (2) testing the degree of interpretation of each variable as the complexity of iterations changes, and (3) obtaining partial dependence plots, among other methods. The findings of this study also suggest that land use types—including facility points—demonstrate differing degrees of influence at two geographical scales: local level and neighborhood level. In the ranking of relative importance at both scales, the variables of education institutions, traffic lights, and service institutions are all ranked high—with a more significant influence on the occurrence of accidents. However, residential land and land use mix variables differed significantly in both scales and showed a significant deviation compared to the other results. When adjusting the complexity of the decision tree, the local level is more suitable for measuring variables such as residential areas and green parks where pedestrians and vehicles have fixed mobility periods and moderate flows. On the contrary, the nearest neighborhood level is more suitable to a small number of variables related to public service facilities at fixed locations, such as traffic lights and bus stops. In the partial dependence plots, all variables, except educational institutions and residences, show a positive correlation for accidents in the fitting process. The results of this study can ideally help inform transportation planners to reconsider transport accident occurrence rates in the context of the proximity to various land use types and public service facilities.

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In this paper, we investigate the issues of initialization and deployment of wireless sensor networks (WSNs) under IEEE 802.11b/g interference and fading channels using frequency hopping (FH). We propose an FH algorithm for WSNs, which is implemented and tested with a pair of nodes employing IPv6 over low power wireless personal area networks (6LoWPAN) standard. The merits and demerits of the proposed FH scheme in WSNs are studied under strong IEEE 802.11b/g interference and frequency selective fading channels. We compare the performance results of the proposed FH scheme with those obtained by single-channel radio in WSNs, and show that FH maintains very reliable data rates in the presence of adverse conditions where the single-channel radio fails. We determine a minimum center frequency offset of channels between IEEE 802.15.4 and IEEE 802.11b/g-based networks, which guarantees the error free network operation of IEEE 802.15.4 using a single channel. We design a second FH procedure comprising only four free channels (15, 20, 25, and 26) of IEEE 802.15.4 standard, and show that in the presence of nearby IEEE 802.11b/g interference, the IEEE 802.15.4 data rate using this method is always 98% and more.

This paper explores door-to-door commuting pa erns and the way commuting time is associated with three factors: the built environment, transport mode (from residence and workplace to HSR stations), and commute frequency. Econometric and statistical analyses are employed to examine evidence from China that draws on a survey targeting Suzhou-based HSR commuters who travel to work in Shanghai. The findings present three major points. First, a dense urban environment around residence and workplace is associated with reduced commuting time to high-density healthcare facilities (Suzhou and Shanghai) and financial institutions (Shanghai only). However, the density of public transport facilities near both residence and workplace has no association with commuting time. Second, taking the metro to and from HSR stations shows signi ficant association with increased commuting time for the first and last miles, while walking from HSR stations to the workplace shows signi ficant reduction of commuting time. Third, daily commuting is associated with reduced commuting time in the first mile, while weekly commuting is reversely related to longer commuting time in the last mile, which is coupled with a shorter commuting time for the first mile than the last mile. These findings lead us to conclude that reducing the total commuting time for a door-to-door journey is a key factor in associated commuting pa erns, commuting frequency, and travel mode choice. This re flects the choices commuters make in relation to where they live rather than where they work, which off ers fewer options. A longer last mile relates to a weekly commuting pa ern rather than a daily commuting. The current public metro systems in both home and work cities appear to be lengthy and inefficient. Transitoriented and integrated development is required to provide more efficient experiences for commuters.