Traffic congestion estimation service exploiting mobile assisted positioning schemes in GSM networks

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

“…The measurement of urban traffic congestion has always been a hot issue in the academic field. Different scholars have proposed different measurement standards and models, but mainly combining urban road network density, total population, traffic flow, road occupancy, number of buses, and other indicators [55,56]. From the perspective of road supply and demand, we regard road area and bus quantity as the supply items of urban traffic (positive indicator), while urban population as the demand item (negative indicator).…”

Bayesian framework for mobility pattern discovery using mobile network events

“…They modeled the mobility patterns statistics with sets of distributions within the context of specific applications. Most literature in network-based mobility mapping, such as [7]- [9] focused on macro level mobility pattern analysis (in which movement of huge amount of people together is considered). The novelty of the present study is a focus on micro-, NE-based mobility pattern analysis, which enables phone operators to offer tailored services to individual and public customers.…”

Exploring the impact of traffic congestion on CO2 emissions in freight distribution networks

“…The 'conventional' road traffic data sources used for logistics system analyses are interviews/surveys [23], traffic information provided by private and/or public authorities using 'in situ' technologies, such as detectors located along the roadside or vehicle counts [24], and floating car data/fleet management systems [25]. In the latter case, GPS or mobile phone devices are inside the moving vehicles recording spatially and temporally the itinerary.…”

“…In the latter case, GPS or mobile phone devices are inside the moving vehicles recording spatially and temporally the itinerary. Recently, Kellner [26] listed problems that will arise when measuring congestion effects in large-scale logistics systems, such as a distribution network, by means of the conventional data sources: Concerning in situ technologies, traffic sensors are expensive to install and maintain and they are therefore not widespread [25]. A nationwide observation of traffic conditions and CO 2 emissions, covering the whole distribution network, is not possible when relying only on traffic information collected by the means of in situ technologies.…”

“…To overcome these problems, this study makes use of traffic data provided by an online navigation and traffic information service, such as Bing Maps, Google Maps, Inrix, Here, and TomTom. The traffic information offered by these services is valuable for at least four reasons [26]: (1) As there are concerns about the accuracy of installed detectors and floating car data [25,28], some navigation service providers use complementary solutions combining different data sources in order to improve the reliability and accuracy of the delivered trip/traffic information (e.g., historic data records, GPS measurements, local experts' knowledge, camera imagery, road sensors) [29,30]. 2In contrast to in situ technologies, navigation data allow for an area-wide observation of traffic conditions as it processes data from in situ technologies and floating car data.…”