Correlation Between Mobility and COVID-19 Cases in Surabaya City, Indonesia

The number of COVID-19 cases in Surabaya was one of the highest in Indonesia at the beginning of the pandemic. This study aims to determine the mobility and activity patterns of the people of Surabaya during the COVID-19 pandemic and find a correlation between people mobility and the number of COVID-19 cases in Surabaya City using Pearson’s Coefficient of Correlation (PCC). The data used are mobility data at Gubeng Station, Purabaya Terminal, Waru Utama toll gate, and COVID-19 Community Mobility Reports. The mobility pattern of the people of Surabaya City in 2020 is divided into 5 phases, namely the normal condition phase (F0), the pandemic’s initial phase (F1), the PSBB phase (F2), the transition of AKB phase (F3), and the AKB phase (F4). This study indicates that the number of people in transit stations and residential areas has a high correlation with the number of COVID-19 cases. In addition, the type of mobility that has the most effect on increasing the number of COVID-19 cases is the mobility of bus transportation

Nodes and Streets: Exploring the Pedestrian Mobility Pattern in the Intersection of the Streets in Dhaka City

A viable pedestrian movement has always been a challenging for the urban planners and designers. The modern perception is to create a mobility towards pedestrian environment and at the same time limiting the dependency on vehicular movement. Pedestrians plays an important key role on reshaping nodes and the streets. The mental mapping of a pedestrian guides him to mobilize from one point to another and creates a pattern of an individual. When hundreds of points are created by the pedestrians a new order of network has created and different functions are intervened often to support them. These changes are responsible for the urban fabric to create a certain dimension and a vibrant network of movement. Dhaka, the capital city of Bangladesh is thriving on vehicular dependency movement but majority of the population are still pedestrians and depends only on public transports. Pedestrian population is dramatically increased due to constant migration of labor incentive market, better employment and fast moving lifestyle. But although the pedestrians are responsible for the vibrant environment due to their mobility pattern, a major upheaval also occur for the unplanned and haphazard street functions on nodal points to serve the pedestrians. In this paper, evaluates the causes of pedestrian movement pattern, illustrate the problems and ineffective functions that creates a node. And finally on the basis of analysis, an outcome of urban node principle has been proposed, serving more effective movements and holistic kind of functional urbanism.

Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.

Mobility Pattern Changes in Indonesia in Response to COVID-19

All countries affected by the COVID-19 pandemic have established several policies to control the spread of the disease. The government of Indonesia has enforced a work-from-home policy and large-scale social restrictions in most regions that result in the changes in community mobility in various categories of places. This study aims to (1) investigate the impact of large-scale restrictions on provincial-level mobility in Indonesia, (2) categorize provinces based on mobility patterns, and (3) investigate regional socio-economic characteristics that may lead to different mobility patterns. This study utilized Provincial-level Google Mobility Index, Flight data scraped from daily web, and regional characteristics (e.g., poverty rate, percentages of informal workers). A Dynamic Time Warping method was employed to investigate the clusters of mobility. The study shows an intense trade-off of mobility pattern between residential areas and public areas. In general, during the first 2.5 months of the pandemic, people had reduced their activities in public areas and preferred to stay at home. Meanwhile, provinces have different mobility patterns from each other during the period of the large-scale restrictions. The differences in mobility are mainly led by the percentage of formal workers in each region.