This paper focuses on the synthetic generation of human mobility data in urban areas. We present a novel and scalable application of Generative Adversarial Networks (GANs) for modeling and generating human mobility data. We leverage actual ride requests from ride sharing/hailing services from four major cities in the US to train our GANs model. Our model captures the spatial and temporal variability of the ride-request patterns observed for all four cities on any typical day and over any typical week. Previous works have succinctly characterized the spatial and temporal properties of human mobility data sets using the fractal dimensionality and the densification power law, respectively, which we utilize to validate our GANs-generated synthetic data sets. Such synthetic data sets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility and intelligent transportation. Ride sharing or hailing services have disrupted urban transportation in hundreds of cities around the globe (Times, 2018; BID2 . In United States, it has been estimated that between 24% to 43% of the population have used ride-sharing services in 2018 BID21 . Uber alone operates in more than 600 cities around the globe BID22 . Ride sharing services have turned urban transportation into a convenient utility (available any place at any time), and become an important part of the economy in large urban areas BID8 .Ride request data from ride sharing services can potentially be of great value. Data gathered from ride sharing services could be used to provide insights about traffic and human mobility patterns which are essential for intelligent transportation systems. Ride requests in major cities with high penetration by such services exhibit spatial and temporal variability. Modeling of such variability is a challenging problem for researchers. Moreover , there are still unresolved challenges, such as: optimal algorithms for dynamic pooling of ride requests BID1 , real-time preplacement of vehicles BID12 , and city scale traffic congestion prediction BID17 and avoidance. Access to large amount of actual ride request data is essential to understanding and addressing these challenges.Data from ride sharing services have been used for real-time sensing and analytics to yield insights on human mobility patterns BID25 BID11 . Each city exhibits a different pattern of urban mobility -there could be cultural or economical factors governing these patterns. If ride sharing services constitute a significant percentage of the riders in a city, can we build models from ride request data to model urban mobility for the whole city and provide societal benefit without compromising personal privacy? This question motivates us to explore the potential of using Generative Adversarial Networks (GANs) to generate synthetic ride request data sets that exhibit very similar attributes as the actual ride request data sets.This work proposes a novel approach of generating synthetic ride request data sets using GANs. This approach involves viewing ride requests as a (temporal) sequence of (spatial) images of ride request locations. The approach uses GANs to match the properties of the synthetic data sets with that of real ride request data sets. Many recent works using neural networks have looked at demand prediction BID29 BID30 and traffic prediction at intersections BID28 . In our work, we are looking at generating actual ride requests for both spatially and temporally granular intervals. Also, we compare and validate the spatial and temporal variations of the DISPLAYFORM0 Figure 1: Ride requests for a small region of downtown San Francisco for a typical week day. Each figure shows the aggregated ride-locations (red dots) over a period of an hour. Each red dot may represent one or more ride-locations. Ride density varies spatially and temporally.synthetic data sets with the real data sets. In dealing with large amount of data for many cities and long training times for GANs, we develop effective ways to parallelize and scale our GANs training runs using large CPU clusters on AWS. We present our GANs scaling approach and experimental results, and show that significant reduction in training times can be achieved. The emergence of ride sharing services and the availability of extensive data sets from such services are creating unprecedented opportunities for: 1) doing city-scale data analytics on urban transportation for supporting Intelligent Transportation Systems (ITS); 2) improving the efficiency of ride sharing services; 3) facilitating real-time traffic congestion prediction; and 4) providing new public services for societal benefit. Moreover, the power of neural networks for machine learning has allowed the creation of useful models which can capture human behavior and dynamic real-world scenarios. The key contributions of this paper include:• We map the ride requests of ride sharing services into a time sequence of images that capture both the temporal and spatial attributes of ride request patterns for a city.• Based on extensive real world ride request data, we introduce a GANs based workflow for modeling and generating synthetic and realistic ride request data sets for a city.• We further show that our GANs workload can be effectively scaled using Xeon CPU clusters on AWS, in reducing training times from hours to minutes for each city.• Using previous work on modelling urban mobility patterns, we validate our GANs generated data sets for ride requests for four major US cities, by comparing the spatial and temporal properties of the GANs generated data sets against that of the real data sets.There are other promising avenues for further research. Some open research topics include: Figure 6 : Plots for four cities highlighting the temporal variability of ride requests visible in both real and our model (predicted) for ride request generation. The pattern is representative of any typical day of week.• Using the GANs generated data sets for experiments on new algorithms for dynamic ride pooling, real-time pre-placement of vehicles, and real-time traffic congestion prediction.
This paper focuses on the synthetic generation of human mobility data in urban areas using GANs. 
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