Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns

Houston, Douglas; Luong, Thuy T.; Boarnet, Marlon G.

doi:10.1016/j.trc.2014.08.013

Cited by 35 publications

(21 citation statements)

References 30 publications

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“…The 5-minute threshold is selected to reflect the long signal light waiting time in Beijing and reduce false positive trip separations when vehicles stop at traffic lights or idle in traffic. Although trip start/end time and locations need to be inferred from the data, substantial agreement exist between GPS-derived travel patterns and self-reported ones [24]. Using a different threshold (e.g.…”

Section: Datamentioning

confidence: 99%

Understanding taxi travel patterns

Cai

Zhan

Zhu

et al. 2016

Physica A: Statistical Mechanics and its Applications

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Taxis play important roles in modern urban transportation systems, especially in mega cities. While providing necessary amenities, taxis also significantly contribute to traffic congestion, urban energy consumption, and air pollution. Understanding the travel patterns of taxis is thus important for addressing many urban sustainability challenges. Previous research has primarily focused on examining the statistical properties of passenger trips, which include only taxi trips occupied with passengers. However,

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Section: Datamentioning

confidence: 99%

Understanding taxi travel patterns

Cai

Zhan

Zhu

et al. 2016

Physica A: Statistical Mechanics and its Applications

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“…For example, innovative systems, such as the Internet of Vehicles, may collect data, such as vehicle speed and vehicle operations, using Global Positioning System (GPS) and sensors located in the car. This information is transmitted to the urban traffic brain through Internet technology to monitor road traffic conditions and intelligently adjust the cycle of signal lights (to better serve to alleviate traffic congestion) [42][43][44][45][46][47]. However, this information can also be used to assess driver insurance risks, with adverse consequences for privacy rights.…”

Section: Research Hypothesesmentioning

confidence: 99%

Could a Smart City Ameliorate Urban Traffic Congestion? A Quasi-Natural Experiment Based on a Smart City Pilot Program in China

2020

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More countries and regions are joining the bandwagon of smart city construction, which is an important strategy and innovative urban governance concept to solve the problem of rapid urbanization. This paper examines whether smart city innovation is able to ameliorate the traffic congestion faced by a large number of cities. Using panel data for 187 prefecture-level cities in China from 2008 to 2017, this paper tests the effect of implementation of a smart city on urban traffic congestion with the difference-in-difference method. The results show that, firstly, the construction of smart cities have significantly reduced the degree of urban traffic congestion and improved the quality and capacity of public transport. Secondly, information technology and urban innovation are the main mechanisms for smart city implementation to improve urban traffic problems. Thirdly, the improvement effect of smart city implementation on traffic management shows an increasing marginal effect over time. By overcoming the estimation bias in previous studies, this study accurately analyzes the positive role and dynamic effect of smart city construction on traffic improvement. It augments the literature of program evaluation and assessment of smart city implementation. By examining how to improve traffic congestion, it offers some insights that could inspire governments to build smarter cities with better traffic.

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“…GPS device allows to track and to monitor people's daily movements and commuting patterns and provides information about geographic coordinates, time, distance, speed, altitude [12]. GPS tracking devices are used in combination with travel/activity diaries or accelerometer to improve the measurements of daily movement patterns and exposure assessment [13,14]. GPS technology reduces the measurement bias and collects more precise data of time-activity patterns compared to traditional self-reported surveys [15].…”

Section: Introductionmentioning

confidence: 99%

Effects of Individual and Environmental Factors on GPS-Based Time Allocation in Urban Microenvironments Using GIS

et al. 2018

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Time-activity patterns are an essential part of personal exposure assessment to various environmental factors. People move through different environments during the day and they have different daily activity patterns which are significantly influenced by individual characteristics and the residential environment. In this study, time spent in different microenvironments (MEs) were assessed for 125 participants for 7 consecutive days to evaluate the impact of individual characteristics on time-activity patterns in Kaunas, Lithuania. The data were collected with personal questionnaires and diaries. The global positioning system (GPS) sensor integrated into a smartphone was used to track daily movements and to assess time-activity patterns. The study results showed that behavioral and residential greenness have a statistically significant impact on time spent indoors. These results underline the high influence of the individual characteristics and environmental factors on time spent indoors, which is an important determinant for exposure assessment and health impact assessment studies.

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Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns

Cited by 35 publications

References 30 publications

Understanding taxi travel patterns

Understanding taxi travel patterns

Could a Smart City Ameliorate Urban Traffic Congestion? A Quasi-Natural Experiment Based on a Smart City Pilot Program in China

Effects of Individual and Environmental Factors on GPS-Based Time Allocation in Urban Microenvironments Using GIS

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