21 research outputs found
TPM: A GPS-based Trajectory Pattern Mining System
With the development of big data and artificial intelligence, the technology
of urban computing becomes more mature and widely used. In urban computing,
using GPS-based trajectory data to discover urban dense areas, extract similar
urban trajectories, predict urban traffic, and solve traffic congestion
problems are all important issues. This paper presents a GPS-based trajectory
pattern mining system called TPM. Firstly, the TPM can mine urban dense areas
via clustering the spatial-temporal data, and automatically generate
trajectories after the timing trajectory identification. Mainly, we propose a
method for trajectory similarity matching, and similar trajectories can be
extracted via the trajectory similarity matching in this system. The TPM can be
applied to the trajectory system equipped with the GPS device, such as the
vehicle trajectory, the bicycle trajectory, the electronic bracelet trajectory,
etc., to provide services for traffic navigation and journey recommendation.
Meantime, the system can provide support in the decision for urban resource
allocation, urban functional region identification, traffic congestion and so
on
TTDM: A Travel Time Difference Model for Next Location Prediction
Next location prediction is of great importance for many location-based
applications and provides essential intelligence to business and governments.
In existing studies, a common approach to next location prediction is to learn
the sequential transitions with massive historical trajectories based on
conditional probability. Unfortunately, due to the time and space complexity,
these methods (e.g., Markov models) only use the just passed locations to
predict next locations, without considering all the passed locations in the
trajectory. In this paper, we seek to enhance the prediction performance by
considering the travel time from all the passed locations in the query
trajectory to a candidate next location. In particular, we propose a novel
method, called Travel Time Difference Model (TTDM), which exploits the
difference between the shortest travel time and the actual travel time to
predict next locations. Further, we integrate the TTDM with a Markov model via
a linear interpolation to yield a joint model, which computes the probability
of reaching each possible next location and returns the top-rankings as
results. We have conducted extensive experiments on two real datasets: the
vehicle passage record (VPR) data and the taxi trajectory data. The
experimental results demonstrate significant improvements in prediction
accuracy over existing solutions. For example, compared with the Markov model,
the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi
data
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
A fundamental question in any peer-to-peer ride-sharing system is how to,
both effectively and efficiently, meet the request of passengers to balance the
supply and demand in real time. On the passenger side, traditional approaches
focus on pricing strategies by increasing the probability of users' call to
adjust the distribution of demand. However, previous methods do not take into
account the impact of changes in strategy on future supply and demand changes,
which means drivers are repositioned to different destinations due to
passengers' calls, which will affect the driver's income for a period of time
in the future. Motivated by this observation, we make an attempt to optimize
the distribution of demand to handle this problem by learning the long-term
spatio-temporal values as a guideline for pricing strategy. In this study, we
propose an offline deep reinforcement learning based method focusing on the
demand side to improve the utilization of transportation resources and customer
satisfaction. We adopt a spatio-temporal learning method to learn the value of
different time and location, then incentivize the ride requests of passengers
to adjust the distribution of demand to balance the supply and demand in the
system. In particular, we model the problem as a Markov Decision Process (MDP)
A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
The recent emergence of dockless bike sharing systems has resulted in new patterns of urban transport. Users can begin and end trips from their origin and destination locations rather than docking stations. Analysis of changes in the spatiotemporal availability of such bikes has the ability to provide insights into urban dynamics at a finer granularity than is possible through analysis of travel card or dock-based bike scheme data. This study analyses dockless bike sharing in Nanchang, China over a period when a new metro line came into operation. It uses spatial statistics and graph-based approaches to quantify changes in travel behaviours and generates previously unobtainable insights about urban flow structures. Geostatistical analyses support understanding of large-scale changes in spatiotemporal travel behaviours and graph-based approaches allow changes in local travel flows between individual locations to be quantified and characterized. The results show how the new metro service boosted nearby bike demand, but with considerable spatial variation, and changed the spatiotemporal patterns of bike travel behaviour. The analysis also quantifies the evolution of travel flow structures, indicating the resilience of dockless bike schemes and their ability to adapt to changes in travel behaviours. More widely, this study demonstrates how an enhanced understanding of urban dynamics over the “last-mile” is supported by the analyses of dockless bike data. These allow changes in local spatiotemporal interdependencies between different transport systems to be evaluated, and support spatially detailed urban and transport planning. A number of areas of further work are identified to better to understand interdependencies between different transit system components
A dynamic approach to rebalancing bike-sharing systems
Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule
Environmental benefits of bike sharing: A big data-based analysis
Bike sharing is a new form of transport and is becoming increasingly popular in cities around the world. This study aims to quantitatively estimate the environmental benefits of bike sharing. Using big data techniques, we estimate the impacts of bike sharing on energy use and carbon dioxide (CO 2 ) and nitrogen oxide (NO X ) emissions in Shanghai from a spatiotemporal perspective. In 2016, bike sharing in Shanghai saved 8358 tonnes of petrol and decreased CO 2 and NO X emissions by 25,240 and 64 tonnes, respectively. From a spatial perspective, environmental benefits are much higher in more developed districts in Shanghai where population density is usually higher. From a temporal perspective, there are obvious morning and evening peaks of the environmental benefits of bike sharing, and evening peaks are higher than morning peaks. Bike sharing has great potential to reduce energy consumption and emissions based on its rapid development