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  • Mobility and traffic research  (5)
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  • Mobility and traffic research  (5)
  • 1
    Online Resource
    Online Resource
    Wireless Communication Simulation Model for Traffic Monitoring Systems Based on Dynamic Cellular Handoffs : Framework and Evaluation
    SAGE Publications ; 2012
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2291, No. 1 ( 2012-01), p. 26-34
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2291, No. 1 ( 2012-01), p. 26-34
    Abstract: Technology for tracking with handoff-based cellular probes is one of the most important modern methods of traffic monitoring. Some simulation frameworks have been proposed to investigate the key issues in such systems. However, most existing simulation approaches rely on assumptions of cell boundaries and handoff points; these assumptions may lead to inaccurate predictions of the performance of real systems. The proposed model incorporated the actual communication mechanism for cell phone signals used in real-world cellular networks. In the proposed simulation framework, cell boundaries were dynamic, cell sizes varied, and the handoff process was controlled by actual protocols for cellular communication. An algorithm for handoff pattern matching is also presented. The algorithm deals with the dynamic handoff sequence for the purposes of traffic detection. The approach was verified with a calibrated simulation road network. The impacts of two important factors, call duration and probe penetration, were also investigated. The results showed that the proposed simulation platform for wireless communication could produce more reliable results and gain deeper insights into the performance of the handoff-based cellular traffic monitoring systems than had been achieved through the traditional simulation approaches.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2291-04
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2012
    detail.hit.zdb_id: 2403378-9
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    Online Resource
  • 2
    Online Resource
    Online Resource
    GPS and Acceleration Data in Multimode Trip Data Recognition Based on Wavelet Transform Modulus Maximum Algorithm
    SAGE Publications ; 2015
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2526, No. 1 ( 2015-01), p. 90-98
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2526, No. 1 ( 2015-01), p. 90-98
    Abstract: The GPS-based travel survey is an emerging data collection method in transportation planning. The survey's application in trip mode detection has been explored in many studies. Most research on trip mode detection methods based on GPS data has been developed and tested with data collected from European and American countries. The methods cannot be easily adapted to Asian countries such as China, India, and Japan, which have much higher population densities, more complex road networks, and highly mixed travel modes during daily commuting. Furthermore, for trip segment division in multimode travel, existing algorithms use travel time and distance thresholds that are highly dependent on local travel behavior and lack universality across traffic environments. This paper proposes an innovative framework for detecting trip modes in complex urban environments. First, a smartphone application, GPSurvey, was developed to collect passive GPS trace data. Then a wavelet transform modulus maximum algorithm was developed for trip segment division. The algorithm has outstanding capabilities for identifying singularity features of a signal; this factor suits the task of detecting mode changes in a complex traffic environment. A neural network module was developed for mode detection on the basis of cell phone GPS location and acceleration data. The results indicate that the proposed method has promising performance. The average absolute detection error of mode transfer time was within 1 min, and the accuracy for detecting all modes was greater than 85%.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2526-10
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
    detail.hit.zdb_id: 2403378-9
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data
    SAGE Publications ; 2021
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2675, No. 7 ( 2021-07), p. 454-466
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2675, No. 7 ( 2021-07), p. 454-466
    Abstract: Cellular phone data has been proven to be valuable in the analysis of residents’ travel patterns. Existing studies mostly identify the trip ends through rule-based or clustering algorithms. These methods largely depend on subjective experience and users’ communication behaviors. Moreover, limited by privacy policy, the accuracy of these methods is difficult to assess. In this paper, points of interest data is applied to supplement cellular phone data’s missing information generated by users’ behaviors. Specifically, a random forest model for trip end identification is proposed using multi-dimensional attributes. A field data acquisition test is designed and conducted with communication operators to implement synchronized cellular phone data and real trip information collection. The proposed identification approach is empirically evaluated with real trip information. Results show that the overall trip end detection precision and recall reach 95.2% and 88.7% with an average distance error of 269 m, and the time errors of the trip ends are less than 10 min. Compared with the rule-based approach, clustering algorithm, naive Bayes method, and support vector machine, the proposed method has better performance in accuracy and consistency.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981211031537
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2403378-9
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  • 4
    Online Resource
    Online Resource
    Tour-Based Time-of-Day Choices for Weekend Nonwork Activities
    SAGE Publications ; 2008
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2054, No. 1 ( 2008-01), p. 37-45
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2054, No. 1 ( 2008-01), p. 37-45
    Abstract: Research on time-of-day (TOD) choices has predominantly focused on weekday trips or activities, with few studies covering the behavior of TOD choices on weekends. This paper examines TOD choices on weekends using a tour-based approach. A multinomial discrete choice model was calibrated to explore the effects that household and individual socio-demographics have on TOD choice behavior. In light of unique travel patterns on weekends, the authors have combined both Saturday and Sunday into a single travel period, which is then divided into six TOD segments. The methodology is based on the observation that, unlike on weekdays, what individuals do on Saturdays is less likely to be repeated on Sundays with the same TOD pattern. This manuscript demonstrates the application of a tour-based TOD weekend forecast model using the 2001 Atlanta Household Survey data from Georgia. The survey contains detailed travel information on weekends. The study presents the exploratory analysis of weekend travel patterns at both trip-based and tour-based levels in Atlanta. A brief comparison of trip-based and tour-based models is also given. The study validates the suggestion that a tour-based model improves the overall goodness-of-fit of the model and produces a better forecast.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2054-05
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2008
    detail.hit.zdb_id: 2403378-9
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  • 5
    Online Resource
    Online Resource
    Detection of Traffic Pattern Based on Fuzzy Clustering and Wavelet Analysis Model at Different Signaling Positioning Frequencies
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 8 ( 2022-08), p. 601-618
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 8 ( 2022-08), p. 601-618
    Abstract: Signaling positioning technology provides a new opportunity to understand an individual’s travel characteristics. In recent studies, the travel parameters obtained are mainly macroscopic travel information. However, extracting detailed trip chain information, such as the trip mode and mode-switching time point, remains a challenge. Furthermore, because of the iterative development of wireless networks, existing communication operators usually store different frequencies and accuracy (2G/3G and 4G) of signaling data simultaneously, making the refined identification of travel information more difficult. Therefore, this paper proposes a new method. First, we use the shortest distance algorithm to match the signaling data with the road network. Second, a wavelet transform modulus maximum (WTMM) algorithm is proposed to divide multimodal travel trajectories into single-mode trip segments; thus, spatiotemporal information related to mode transfer can be obtained. Finally, an unsupervised fuzzy kernel c-means clustering (FKCM) algorithm is proposed to distinguish travel modes. As comparison data, smartphone GPS and travel log data are also collected to analyze the detection result and improve the method. The identification errors of mode-switching time points at different frequencies are all less than 360 s. The average correct rate of traffic mode identification for 2G is 65.1%, and the average correct rate of traffic mode identification for 3G is 78.2%. 4G intensive cellular positioning data has a significantly better recognition effect than low-frequency data; the average trip mode detection accuracy reaches 89.6%, and the mode-switching time point detection errors are within 300 s.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221084688
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2403378-9
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    Online Resource
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