Suchergebnisse

Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people's decision to combine modes in a tour.

To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.

Exposure to risk is a theoretically important correlate of crash risk, but many safety performance functions (SPFs) for pedestrian and bicycle traffic have yet to include the mode-specific measures of exposure. When SPFs are used in the systematic approach to assess network-wide crash risk, the omission of the exposure potentially could affect the identification of high-risk locations. Using crash data from Minneapolis, this study constructs and compares two sets of SPFs, one with pedestrian and bicycle exposure variables and the other without, for network-wide intersection and mid-block crash models. Inclusion of mode-specific exposure variables improves model validity and measures of goodness-of-fit and increases accuracy of predictions of pedestrian and bicycle crash risk. Including these exposure variables in the SPFs changes the distribution of high-risk locations, including the proportion of high-risk locations in low-income and racially concentrated areas. These results confirm the importance of incorporating exposure measures within SPFs and the need for pedestrian and bicycle monitoring programs to generate exposure data.

Abstract
Background
Changing water quality was prevalent in the current water supply. The fluctuation of iron stability due to changing water quality followed four characteristics: objectivity, relativity, predictability, and controllability. Therefore, it was necessary to study the stability of iron in the pipe network by integrating different water quality factors.

Results
The iron stability risk evaluation system was established according to the different water quality factors in the drinking water distribution systems (DWDSs). Meanwhile, an improved fuzzy comprehensive evaluation method was established to evaluate the risk of iron. Chloride, sulfate, dissolved organic matter (DOM) and pH were selected as the risk assessment index. The divisions of different evaluation levels were carried out through the values of water quality factor. On the basis of expert scoring, the weight and membership degree of water quality factors were analyzed by structural entropy method. In addition, risk analysis was established by using the optimized risk assessment system. According to the results of the comprehensive evaluation, DOM and pH were identified as two of the most important factors in the evaluation of the iron stability. In addition, compared with the traditional fuzzy comprehensive evaluation method, the optimized method had a higher degree of fit which could more clearly prove the relationship between the risk value and the iron concentration.

Conclusion
The uncertainty between the factors was eliminated by establishment of the fuzzy evaluation method combined with the different effects of water factors on iron stability. The method could be used as a comprehensive evaluation and be beneficial to the analysis of iron risk in water supply network.