Browsing by Subject "Global positioning system (GPS)"
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Item Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data(Journal of Transport and Land Use, 2017) Zhang, Kaisheng; Sun, Daniel (Jian); Shen, Suwan; Zhu, YiWith the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on 24-hour congestion pattern of road segments in urban area, so that the spatial autoregressive moving average model (SARMA) was introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained 12 traffic-related factors and land use factors’ impact on road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use and so on have large impact on congestion formation. The Fuzzy C-means clustering was proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service of traffic from the congestion perspective.Item Impending Box Impact Warning System for Prevention of Snowplow-Bridge Impacts: A Final Report of Investigations(University of Minnesota Center for Transportation Studies, 2009-02) Lindeke, Richar R.; Katmale, Hilal; Verma, RaviEach year, three or four Mn/DOT snowplows suffer bridge/box collisions while plowing. These collisions can shear off the box and frame damage to the truck. The box then falls onto the road surface where it becomes an immediate life-threatening hazard to traffic. In some cases, the integrity of the bridge may also be compromised. A typical collision of this type requires expenditures of $30,000 to $40,000 and results in potentially dangerous delays in achieving clean pavement status along the affected snowplowing route. Feasibility of linking on-board GPS technology for Automatic Vehicle Location with the current bridge information database at Mn/DOT, “BrInfo,” will be investigated, on a plow-route by route basis, to create collision maps. Collision avoidance then will use some primitive form of map matching. In addition, a prototype warning system that serves as a bridge proximity sensor will be developed to alert the snow plow driver that he/she is approaching a bridge with the box at a dangerous height. This warning system is integrated in an on-board box position sensor so that the driver can be alerted that the box must immediately be lowered. While realizing that additional means for box height control may complicate snowplow maintenance, any system that relieves the driver of cognitive overload, to reduce driver stress and fatigue during plowing operating, when running extended rural plow routes, needs to be implemented.Item Modelling route choice of Dutch cyclists using smartphone data(Journal of Transport and Land Use, 2018) Bernardi, Silvia; La Paix Puello, Lissy; Geurs, KarstThis paper analyzes the GPS traces recorded by cyclists in the framework of the Mobile Mobility Panel throughout the Netherlands. The objective of this paper is to analyze bicycle route choice via network attributes and trip length over a sequence of trips by approximately 280 bicycle users, who were asked to register their trips by means of a specific smartphone application. Approximately 3,500 bike trips were recorded throughout the Netherlands over a four-week period in 2014. The bike trips have been matched to a specific bicycle network built and updated by a Dutch cyclists’ union. Route choice models were estimated, using both the binomial logit model and the mixed multinomial logit model with Path-size logit model formulation. The chosen alternatives were part of the choice set for the mixed multinomial logit model. Also, the shortest route was generated for each origin-destination pair. The results show that trip lengths and trip distribution over time reveal a population sample much used to cycling, frequently and over long distances. Furthermore, when considering the composition of chosen routes in terms of link type, the usage of cycleway links is frequent. For repeated trips, the shortest route option tends to be chosen more; frequent cyclists, on systematic trips, tend to optimize their trip and prefer the shortest routes. This is even truer for males and for non-leisure trips. The estimated probabilities for both multinomial and binomial models show that the binomial model tends to overestimate the probabilities of choosing the shortest route. This result is stronger in non-leisure trips, where people tend to choose a more personalized route, instead of the shortest. This research contributes to the generation of a more efficient distribution of bicycle trips over the network. Future research can more specifically address the intrapersonal variation in route—destination choice given the availability of longitudinal data.Item Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model(Journal of Transport and Land Use, 2020) Chen, Xiaoxu; Yang, Chao; Xu, XiangdongTrip mode inference plays an important role in transportation planning and management. Most studies in the field have focused on the methods based on GPS data collected from mobile devices. While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. The method mainly consists of three parts: activity-nodes recognition, travel-time computation, and clustering using the Logarithm Gaussian Mixed Model. Moreover, we compare two other methods (i.e., Gaussian Mixed Model and K-Means) with the Logarithm Gaussian Mixed Model. We conduct experiments using real mobile phone signaling data in Shanghai and the results show that the proposed method can obtain acceptable accuracy overall. This study provides an important opportunity to infer trip mode from the aspect of probability using mobile phone signaling data.