Predicting taxi demand at high spatial resolution: Approaching the limit of predictability

Zhao, Kai; Khryashchev, Denis; Freire, Juliana; Silva, Claudio; Vo, Huy T.

doi:10.1109/bigdata.2016.7840676

Cited by 99 publications

(49 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To attain the accurate and robust short-term passenger demand forecasting, both parametric (e.g., ARIMA) and non-parametric models (e.g., neural network) have been examined. For instance, Zhao et al (2016) implemented and compared three models, i.e., the Markov algorithm, Lempel-Ziv-Welch algorithm, and neural network. In that research, the results showed that neural network performed better with the lower theoretical maximum predictability while the Markov predictor had better performance with the higher theoretical maximum predictability.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Zheng

Chen

2017

Transportation Research Part C: Emerging Technologies

584

261

View full text Add to dashboard Cite

Short-term passenger demand forecasting is of great importance to the ondemand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatiotemporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches in- * cluding both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). Furthermore, the consideration of exogenous variables in addition to passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since it reduces the root mean squared error (RMSE) by 50.9%. It is also interesting to find that the feature selection reduces 30% in the dimension of predictors and leads to only 0.6% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Keywords: On-demand ride services, short-term demand forecasting, deep learning (DL), fusion convolutional long short-term memory network (FCL-Net), long short-term memory (LSTM), convolutional neural network (CNN)

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Zheng

Chen

2017

Transportation Research Part C: Emerging Technologies

584

261

View full text Add to dashboard Cite

show abstract

“…For example, the representative neural network in machine learning, multilayer perceptron (MLP), is used by Mukai et al [25] into their work to analyze and predict taxi demands. Zhou et al [26] even employ three predictors to adapt to areas with different resolutions.…”

Section: Traditional Methodsmentioning

confidence: 99%

Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction

2019

IJGI

View full text Add to dashboard Cite

Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error.

show abstract

“…Our design exploits these two features to manage dispatching vehicles and thus improving ridesharing services. Several previous studies show that it is possible to learn from past taxi data and thus organizing the taxi fleet and minimizing the wait-time for passengers and drivers [41], [42], [43], [27], [15], [44], [45]. Nishant et.…”

Section: Related Workmentioning

confidence: 99%

DeepPool: Distributed Model-Free Algorithm for Ride-Sharing Using Deep Reinforcement Learning

Al-Abbasi

Ghosh

Aggarwal

2019

IEEE Trans. Intell. Transport. Syst.

122

View full text Add to dashboard Cite

The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed. Further, ride-sharing allows sharing costs and, hence, reduces the congestion and emission by making better use of vehicle capacities. In this work, we develop a distributed model-free, DeepPool, that uses deep Q-network (DQN) techniques to learn optimal dispatch policies by interacting with the environment. Further, DeepPool efficiently incorporates travel demand statistics and deep learning models to manage dispatching vehicles for improved ride sharing services. Using real-world dataset of taxi trip records in New York City, DeepPool performs better than other strategies, proposed in the literature, that do not consider ride sharing or do not dispatch the vehicles to regions where the future demand is anticipated. Finally, DeepPool can adapt rapidly to dynamic environments since it is implemented in a distributed manner in which each vehicle solves its own DQN individually without coordination.

show abstract

Predicting taxi demand at high spatial resolution: Approaching the limit of predictability

Cited by 99 publications

References 18 publications

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction

DeepPool: Distributed Model-Free Algorithm for Ride-Sharing Using Deep Reinforcement Learning

Contact Info

Product

Resources

About