Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies 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 dependencies 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 spatio-temporal 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. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% 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.

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

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

We consider an on-demand service platform using earning-sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price-sensitive customers...

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Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers

With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation industry. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensitive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform and many other factors. Diverse variables and factors in the system are strongly endogenous and interactively dependent. How to design and operate ridesourcing systems is vital—and challenging—for all stakeholders: passengers/users, drivers/service providers, platforms, policy makers, and the general public. In this paper, we propose a general framework to describe ridesourcing systems. This framework can aid understanding of the interactions between endogenous and exogenous variables, their changes in response to platforms’ operational strategies and decisions, multiple system objectives, and market equilibria in a dynamic manner. Under the proposed general framework, we summarize important research problems and the corresponding methodologies that have been and are being developed and implemented to address these problems. We conduct a comprehensive review of the literature on these problems in different areas from diverse perspectives, including (1) demand and pricing, (2) supply and incentives, (3) platform operations, and (4) competition, impacts, and regulations. The proposed framework and the review also suggest many avenues requiring future research.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5517&context=sis_research

Ridesourcing systems: A framework and review

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

In this paper, we present an ensemble learning approach for better understanding ridesplitting behavior of passengers of ridesourcing companies who provide prearranged and on-demand transportation services. An ensemble learning model is a weighted combination of multiple classification models or week classifiers to form a strong classification model. The goal of ensemble learning is to combine decisions or predictions of several base classifiers to improve prediction, generalizability, and robustness over a single classifier. This paper employs the Boosting ensemble by growing individual decision trees sequentially and then assembling these trees to produce a powerful classification model. To improve the prediction accuracy of ridesplitting choices, we explored real-world individual level data extracted from the on-demand ride service platform of DiDi in Hangzhou, China. Over one million trips of the four service types, i.e., Taxi Hailing Service, Express, Private Car Service, and Hitch, are explored with descriptive statistics. A variety of features that may impact ridesplitting behavior are ranked and selected by using the ReliefF algorithm, such as trip travel time, trip costs, trip length, waiting time fee, travel time reliability of origins/destinations and so on. The Boosting ensemble trees with full features and selected features are trained and validated using two independent datasets. This paper also verifies that ensemble learning is particularly useful and powerful in the ridesplitting analysis and outperforms three other widely used classifiers. This paper is one of the first quantitative studies that empirically reveal the real-world demand and supply pattern by exploring the city-wide data of an on-demand ride service platform.

Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

Traffic volume is a fundamental measurement in traffic analyses. In mixed traffic, vehicles vary in size, length, headway, spacing, and acceleration/deceleration. Therefore, if we can categorize th...

Measuring the Passenger Car Equivalent of Small Cars and SUVs on Rainy and Sunny Days

This paper presents a big data driven simulation-based dynamic traffic assignment (DTA) model by combining cellular signaling data, remote microwave sensors and license plate recognition (LPR) cameras data. First, the static origin destination (OD) matrix is estimated by using cellular signaling data. Second, remote microwave and LPR data are fused to calibrate parameters on the network supply side, including the link fundamental diagram calibration and link impedance function calibration. Third, a mesoscopic simulation model is developed by using DTALite, and the dynamic OD calibration is conducted by comparing link flows generated by simulation-based DTA and measured by remote microwave sensors. Finally, this paper takes the real-world road network of Hangzhou as an example to quantitatively assess impacts of large-scale implemented work zones before the 2016 G20 Summit and adverse weather conditions on the road network performance with the developed model. Results show the big data driven simulation-based DTA model developed in this paper has wide prospects of applications in the evaluation and optimization of comprehensive transportation management strategies.

Cellular signaling data driven simulation-based dynamic traffic assignment and its applications to a real-world road network

Taxis are considered one of the symbols of urban transportation systems due to their large daily traveling mileage. Taking into account hatchback cars with two rows of seats, this paper presents a new approach to taxi efficiency by decreasing the vehicle size. The policy’s influences on fuel consumption, emission, safety, and economic efficiency are comprehensively analyzed. With supplementary surveys of taxi passengers, a total of 1110 taxi trips are observed at nine busy locations. The results show that the trunk is not used in 88% of taxi trips in Hangzhou. It is estimated that downsizing each taxi results in annual decreases in fuel consumption by 1600 L, CO by 311.9 kg, HC by 15.4 kg, and NOx by 8.9 kg. By using a small car as a taxi instead of a standard car, the annual fixed cost plus the fuel cost will be reduced by 38%. By analyzing GPS data of 7081 taxis in Hangzhou, we found a network-wide average taxi speed of 23 km/h. Since the average speed of taxis is relatively low in urban areas, the use of small cars could provide drivers with higher maneuver capability, while increasing the safety of small cars. According to the findings of this paper, using small cars will increase the efficiency and sustainability of the taxi industry.

https://www.mdpi.com/2071-1050/11/18/4944/pdf

Majid Zahiri

Papers

Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

Measuring the Passenger Car Equivalent of Small Cars and SUVs on Rainy and Sunny Days

Cellular signaling data driven simulation-based dynamic traffic assignment and its applications to a real-world road network

Taxi Downsizing: A New Approach to Efficiency and Sustainability in the Taxi Industry