Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal

Road Network Design in a Developing Country Using Mobile Phone Data: An Application to Senegal

“…The line extended to Bignona passes through Gambia, where the Trans-Gambia ferry service is included, and thus a bridge is suggested to be built to replace that ferry service. The connection between Tivaouane (7) and Bambey (10) and the connection between Fatick (12) and Foundiougne (13) are found as the most important regional roads for the accessibility in the country; therefore, they are suggested to be upgraded to national roads. Moreover, a link is suggested to be added between Thies (5) and Mbour (6).…”

“…Transport researchers are mostly interested in location and timestamps of mobile phone activities, usually designated as mobile phone traces, since such information can be a proxy for users' movements in space and time [6]. Such traces have been used for many purposes, including estimation of travel demand [7], exploration of urban structures and dynamics [6], [8], [9], detection of road traffic status [10]- [12], and analysis of human activities [13]- [15]. Apart from location and time information, the data of telecommunication, such as caller ID and recipient ID of a call, is sometimes available in some mobile phone data as well.…”

Understanding Urban Human Mobility through Crowdsensed Data

“…A scatter plot on a map is used to visualize trajectories and important locations, such as the trip example in [11] and base station map in [12]. A bar chart or histogram is often used to study mobility attribute values that are accumulated in consecutive time intervals, such as the histogram of flow densities in [1]. A heatmap can reveal knowledge about spatial or temporal distributions of certain type of human activities, such as the spatial distribution of user counts in [4] and temporal distribution of journey time in [13].…”

Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data

“…, and 90%, respectively (Figure 4b). In this paper, we generated the quantile vector of break points Q = [−317, −128, −53, −18, −1, 15,51,122,314]. We use Q to classify each n i, j of N into different groups and assign it a level label to represent the intensity of convergence or divergence as shown in Table 2.…”

“…In addition, based on the temporal convergence and divergence patterns of human mobility in different urban regions, managers can optimize urban public bicycle dock locations or real-time bicycle schedules in convergent and divergent areas to maintain a balance between supply and demand [50]. Similarly, taxi companies can allocate taxis in locations with high human convergence and divergence activities at specific times of a day [51]. Therefore, these findings can be used to improve urban public transport efficiency, which helps promote intelligent urban mobility [52,53].…”