- freely available
ISPRS Int. J. Geo-Inf. 2019, 8(10), 434; https://doi.org/10.3390/ijgi8100434
- We develop an integrated geovisual analytics approach that integrates two advanced machine learning methods with interactive maps to characterize two types of high-level, complex transit travel behavior patterns, including a clustering algorithm to identify transit corridors and a graph-embedding algorithm to identify hierarchical mobility community structure.
- We design novel integrated geovisual analytics interfaces for the discovered complex transit movement patterns, including specific views to visualize identified mobility communities and corridors, allowing regular users to examine and understand these ever-changing patterns at different scales and perspectives.
3.1. Methodology Overview
- Global pattern discovery task 1: discover hierarchical mobility structure based on transit trip data and analyze their inter-correlations;
- Global pattern discovery task 2: identify significant transit corridors for any specified time intervals;
- Local pattern exploration task 1: explore the intrinsic information of identified individual transit corridors;
- Local pattern exploration task 2: examine the temporal evolution of the discovered mobility community structure;
- Comprehensive analysis task: design and implement linked or integrated views to visually analyze different components of public transit services (including corridors, community structures, and stops) and discovered travel patterns.
3.2. Data Pre-Processing and Trip Reconstruction
3.3. Extracting Transit Corridors
- Network modeling. The public transit network can be modeled as a directed graph and is mapped to the road network G (V, E), where V denotes the sets of road intersections Vr and transit stops Vt (Vt have been projected into road links), E denotes road segments between road intersections and transit stops. We extract a small set of connected segments Ec whose end nodes have a large shared “accumulated transit flow” and identify them as transit corridors.
- Computing accumulated transit flow. For each node v in Vt, the number of passengers who board at v or before it is recorded as nv. For each passenger, the number of stops she has passed after boarding is recorded until she exits from the vehicle. Then for each v, this number is used as the “accumulated transit flow” at(v).
- Corridor initialization. We choose nodes with a significant number of accumulated transit flows as seeds to grow corridors.
- Corridor expansion. The seed nodes are stacked into a priority queue, ranked by its accumulated transit flow. The one with highest at(v) is popped out and used as the initial seed s0 to expand a corridor. From s0, the algorithm searches for one adjacent stop, s1, that meets the criterion of significant “shared accumulated transit flow” between s0 and s1. “Shared accumulated transit flow” is defined as sa(0→1) = [at(1)− at(0)] /at(0), i.e., the change ratio of accumulated transit flow for the two adjacent stops/nodes. Meanwhile, the two nodes must meet another criterion, namely, “shared transit flow”, which is defined as st(0→1) = n0⋂n1. If the two nodes meet both criteria, the algorithm expands a corridor from node 0 to 1. This procedure repeats until no downstream nodes meet the two criteria. Then another seed in the queue is fetched to grow another corridor, until all seeds are popped.
- Corridor pruning. We need to prune short corridors (with less than 4 stops) or non-significant corridors (transit flows are less than a pre-defined threshold).
- Corridor merging. This final step is to merge corridors if they are already connected or overlapped.
3.4. Discovering Mobility Communities
3.5. Visual Analytics Design
3.5.1. Mobility Communities
3.5.3. Transit Stops
3.5.4. Correlations between Corridors and Communities
4. Implementation and Prototype
5. Analysis and Discussion
5.1. Geovisual Analytics Workflow and Examples
5.2. User Evaluation
- It offers an efficient and effective method to explore a massive amount of transit trips, which is otherwise challenging to analyze and visualize. Based on discovered corridors and mobility communities, we can focus on the most significant travel patterns while still having the capability to explore the details of any stop.
- It delivers an intuitive user interface to combine multiple views that allows regular users to analyze complex transit travel behaviors from different perspectives. For example, corridors present high-level representations of concentrated trips based on road networks, whereas mobility communities are produced to synthesize similar travel characteristics over the partition of the study region.
- It is beneficial for many transit management applications, such as demand modeling, transit planning, and daily operations, since they provide an applicable approach to highlight aggregated movement patterns at multiple spatial and temporal resolutions. The prototype can also be used by regular passengers to plan their transit trips and choose their residence or work place.
6. Conclusions and Further Work
Conflicts of Interest
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