Developing a trip distribution model for identified mobility groups using big data

Abstract

The need for frequent transportation planning has become a key factor since people started becoming more mobile making urban traffic patterns more complex. The primary source for analysing such travel behavior is through manual surveys. These surveys are expensive, time consuming and often are outdated by the time the survey is completed for analysis. To overcome these issues, Mobile Network Big Data (MNBD) which concerns large data sets can be used over such traditional data collection processes. Call Detail Records (CDR) which is a subset of MNBD is readily available as most of the telecommunication service providers maintain CDR. Thus, analyzing CDR leads to an efficient identification of human behavior and location. However, many researches on CDRs have been done focusing to identify travel patterns in order to understand human mobility behavior. Relatively high percentage of sparse data and other scenarios like the Load Sharing Effect (LSE) causes difficulties in identifying precise location of the user when using CDR data. Existing approaches for identifying precise user location patterns have certain constraints. Past researches utilizing CDRs have used primary approaches in recognizing load sharing effects and have given minimum consideration to the transmission power of the respective cell towers when localizing the users. Furthermore, these studies have neglected the differences in mobility behavior of different segment of users and taken the entire community of users as a single cluster. In this research, a novel methodology to overcome these limitations is introduced for locating users from CDRs by dividing the users into distinct clusters for identifying the model parameters and through enhanced identification of load sharing effects by taking the transmission power into consideration. Further, this study contributes to the transport sector by identifying secondary activities from CDR data, without limiting to the primary activity recognition. This research uses approximately 4 billion CDR data points, voluntarily collected mobile data and manually collected travel survey data to find techniques to overcome the existing limitations and validate the results. Proposed dynamic filtering algorithm for load shared records identification showed a significant improvement on accuracy over previous predefined speed based filtering methods. Further, we found that, IO-HMM outperforms standard HMM results on activity recognition.