Search Results (7)

Mobile phones have achieved a high rate of penetration and gained great interest in the field of travel behavior studies. However, mobile phone data exploitation for national travel models has only been sporadically studied thus far. This work focuses on one of the most extensive cellular surveys of its kind carried out thus far in the world, which was performed for two years between 2018 and 2019 with the participation of the two largest cellular providers in Israel, as well as leading GPS companies. The large-scale cell phone survey covered half the population using cellphones aged 8+ in Israel and uncovered local and national trip patterns, revealing the structure of nationwide travel demand. The methodology consists of the following steps: (1) plausibility and quality checks for the data of the mobile operators and the GPS data providers; (2) algorithm development for trip detection, home/work location detection, location and time accuracy, and expansion factors; (3) accuracy test of origin–destination matrices at different resolutions, revisions of algorithms, and reproduction of data; and (4) validation of results by comparison to reliable external data sources. The results are characterized by high accuracy and representativeness of demand and indicate a strong correlation between the cellular survey and other reliable sources. Full article

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities. Full article

Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners. Full article

Rapid demographic ageing is a global challenge and has tremendous implications for transportation planning, because the mobility of elderly people is an essential element for active ageing. Although many studies have been conducted on this issue, most of them have been focused on aggregated travel patterns of the elderly, limited in spatiotemporal analysis, and most importantly primarily relied on sampled (2–3%) household travel surveys, omitting some trips and having concerns of quality and credibility. The objectives of this study are to present more in-depth analysis of the elderly’s spatiotemporal activity and travel behaviors, to compare them with other age and gender groups, and to draw implications for sustainable transportation for the elderly. For our analysis, we used locational trajectory-based mobile phone data in Gangnam, Korea. The data differs from sampled household travel survey data, as mobile phone data represents the entire population and can capture comprehensive travelers’ movements, including peculiarities. Consistent with previous researches, the results of this study showed that there were differences in activity and travel patterns between age and gender groups. However, some different results were obtained as well: for instance, the average nonhome activity time per person for the elderly was shorter than that of the nonelderly, but the average numbers of nonhome activities and trips were rather higher than those of nonelderly people. The results of this study and advantage of using mobile phone data will help policymakers understand the activities and movements of the elderly and prepare future sustainable transportation. Full article

Most humans today have mobile phones. These devices are permanently collecting and storing behavior data of human society. Nevertheless, data processing has several challenges to be solved, especially if it is obtained from obsolete technologies. Old technologies like GSM and UMTS still account for almost half of all devices globally. The main problem in the data is known as neighboring network hit (NNH). An NNH occurs when a cellular device connects to a site further away than it corresponds to by network design, introducing an error in the spatio-temporal mobility analysis. The problems presented by the data are mitigated by eliminating erroneous data or diluting them statistically based on increasing the amount of data processed and the size of the study area. None of these solutions are effective if what is sought is to study mobility in small areas (e.g., Covid-19 pandemic). Elimination of complete records or traces in the time series generates deviations in subsequent analyses; this has a special impact on reduced spatial coverage studies. The present work is an evolution of the previous approach to NNH correction (NFA) and travel inference (TCA), based on binary logic. NFA and TCA combined deliver good travel counting results compared to government surveys (2.37 vs. 2.27, respectively). However, its main contribution is given by the increase in the precision of calculating the distances traveled (37% better than previous studies). In this document, we introduce FNFA and FTCA. Both algorithms are based on fuzzy logic and deliver even better results. We observed an improvement in the trip count (2.29, which represents 2.79% better than NFA). With FNFA and FTCA combined, we observe an average distance traveled difference of 9.2 km, which is 9.8% better than the previous NFA-TCA. Compared to the naive methods (without fixing the $NNHs$), the improvement rises from 28.8 to 19.6 km (46.9%). We use duly anonymized data from mobile devices from three major cities in Chile. We compare our results with previous works and Government’s Origin and Destination Surveys to evaluate the performance of our solution. This new approach, while improving our previous results, provides the advantages of a model better adapted to the diffuse condition of the problem variables and shows us a way to develop new models that represent open challenges in studies of urban mobility based on cellular data (e.g., travel mode inference). Full article

For billing purposes, telecom operators collect communication logs of our mobile phone usage activities. These communication logs or so called CDR has emerged as a valuable data source for human behavioral studies. This work builds on the transportation modeling literature by introducing a new approach of crowdsource-based route choice behavior data collection. We make use of CDR data to infer individual route choice for commuting trips. Based on one calendar year of CDR data collected from mobile users in Portugal, we proposed and examined methods for inferring the route choice. Our main methods are based on interpolation of route waypoints, shortest distance between a route choice and mobile usage locations, and Voronoi cells that assign a route choice into coverage zones. In addition, we further examined these methods coupled with a noise filtering using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and commuting radius. We believe that our proposed methods and their results are useful for transportation modeling as it provides a new, feasible, and inexpensive way for gathering route choice data, compared to costly and time-consuming traditional travel surveys. It also adds to the literature where a route choice inference based on CDR data at this detailed level—i.e., street level—has rarely been explored. Full article

The mobility patterns and trip behavior of people are usually extracted from data collected by traditional survey methods. However, these methods are generally costly and difficult to implement, especially in developing cities with limited resources. The massive amounts of call detail record (CDR) data passively generated by ubiquitous mobile phone usage provide researchers with the opportunity to innovate alternative methods that are inexpensive and easier and faster to implement than traditional methods. This paper proposes a method based on proven techniques to extract the origin–destination (OD) trips from the raw CDR data of mobile phone users and process the data to capture the mobility of those users. The proposed method was applied to 3.4 million mobile phone users over a 12-day period in Mozambique, and the data processed to capture the mobility of people living in the Greater Maputo metropolitan area in different time frames (weekdays and weekends). Subsequently, trip generation maps, attraction maps, and the OD matrix of the study area, which are all practically usable for urban and transportation planning, were generated. Furthermore, spatiotemporal interpolation was applied to all OD trips to reconstruct the population distribution in the study area on an average weekday and weekend. Comparison of the results obtained with actual survey results from the Japan International Cooperation Agency (JICA) indicate that the proposed method achieves acceptable accuracy. The proposed method and study demonstrate the efficacy of mining big data sources, particularly mobile phone CDR data, to infer the spatiotemporal human mobility of people in a city and understand their flow pattern, which is valuable information for city planning. Full article