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Accurate short-term ride-sourcing demand prediction is vital for transportation operations, planning, and policy-making. With the models developed from data based on individual ride-sourcing companies to the joint models with data from multiple ride-sourcing companies, the prediction performance of the proposed models is enhanced significantly. However, the privacy issues of these models become a problem. Raw data collected from individual companies could cause business concerns and data privacy issues. In this article, we propose a Federated Learning (FL) based framework for traffic demand prediction (FedTDP), to solve this problem without sacrificing the prediction performance. In our framework, the model can encapsulate the spatial and temporal correlation of traffic demand data via LSTM and GCN respectively. Moreover, by associating FL with the spatio-temporal model, no raw data is uploaded to the centralized server, and only model parameters are required. Furthermore, a Shapley value-based reward mechanism is proposed to evaluate the contribution of ride-sourcing companies and can be used as a means to distribute rewards accordingly. Finally, a real-world case study of Hangzhou City, China, is conducted. More than 16 million real-world ride-sourcing requests collected from 8 ride-sourcing companies are used, covering most of the ride-sourcing travel demand across the city. The case study shows that the FL-based spatio-temporal model outperforms several well-established prediction models while preserving data privacy. It demonstrates the effectiveness and potential of our proposed framework. Some discussions related to the real-world implementations of the Shapley value-based reward mechanism are also given in the article.

The connected automated vehicles (CAVs) are envisioned to be implemented most likely on electric vehicles, while traditional fuel-powered manually-driven vehicles (MVs) would probably still dominate the automobile market in the next decade. In this context, this paper addresses urban eco-driving of CAVs in mixed traffic and heterogeneous power conditions. The paper aims to develop a practical and deployable eco-driving strategy for CAVs in mixed traffic flow of CAVs and MVs under realistic and complex traffic conditions. Several typical eco-driving scenarios were studied in detail. In a nutshell, the eco-driving strategy for each CAV was determined by solving a typical two-point boundary value problem with minimum electric energy consumption in urban traffic conditions with small market penetration rates (MPRs) of CAVs. A rolling-horizon scheme was applied to implement the eco-driving strategy to handle uncertain/unpredictable disturbances of preceding MVs and the interference of junction queues to the eco-driving maneuvers of CAVs. The paper also studied how eco-driving for electrified CAVs would affect MVs’ fuel consumptions. Simulation studies were carried out on urban arterial roads of multiple signalized intersections in various scenarios of demand and MPR to verify the energy savings effect of the proposed eco-driving strategy. The results showed that via eco-driving electrified CAVs each had a potential of reducing energy consumption by 40%-61%, meanwhile leading to 5%-34% fuel savings on average for each following MV. Further issues concerning the energy saving mechanism of electrified CAVs, impacts of MVs cut-in from adjacent lanes, and passenger comfort were also examined.

Carbon dioxide (CO2) from road traffic is a non-negligible part of global greenhouse gas (GHG) emissions, and it is a challenge for the world today to accurately estimate road traffic CO2emissions and formulate effective emission reduction policies. Current emission inventories for vehicles have either low-resolution, or limited coverage, and they have not adequately focused on the CO2emission produced by new energy vehicles (NEV) considering fuel life cycle. To fill the research gap, this paper proposed a framework of a high-resolution well-to-wheel (WTW) CO2emission estimation for a full sample of vehicles and revealed the unique CO2emission characteristics of different categories of vehicles combined with vehicle behavior. Based on this, the spatiotemporal characteristics and influencing factors of CO2emissions were analyzed with the geographical and temporal weighted regression (GTWR) model. Finally, the CO2emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies. The results show that the distribution of vehicle CO2emissions shows obvious heterogeneity in time, space, and vehicle category. By simply adjusting the existing NEV promotion policy, the emission reduction effect can be improved by 6.5%–13.5% under the same NEV penetration. If combined with changes in power generation structure, it can further release the emission reduction potential of NEVs, which can reduce the current CO2emissions by 78.1% in the optimal scenario.