Electric vehicles (EVs) have been used in the
ride-hailing system in recent years, which brings the electric
fleet management problem (EFMP) critical. This paper
aims to leverage multi-agent reinforcement learning (MARL)
in EFMP. In particular, we focus on how EVs learn to
manage battery charging, pick up and drop off passengers.
We propose an integrated SUMO-Gym framework based on
the SUMO simulator to capture EVs’ asynchronous decisionmaking
regarding charging and ride-hailing in complex traffic
environments. We adopt a hierarchical reinforcement learning
(HRL) scheme, where each EV decides to get charged or pick
up a passenger on the upper level and chooses a charging
station or passenger on the lower level. We develop a learning
algorithm for the HRL scheme to solve EFMP and present
numerical results about the efficiency of our algorithm and
policies EVs have learned in EFMP. Our codes are available at
https://github.com/LovelyBuggies/SUMO-Gym, which provides
an open-source environment for researchers to design traffic
scenarios and test RL algorithms for EFMP.
more »
« less
This content will become publicly available on February 26, 2025
Evaluation of Mixed Electric Fleet for Ride-Hailing Services under California's Clean Miles Standard: A Case Study in San Francisco
Electrifying the ride-hailing services has the
potential to significantly reduce greenhouse gas emissions in
the shared mobility sector. However, these emission
reduction benefits depend on the utilization of EVs to serve
trip requests, especially during the fleet electrification process.
In this paper, we evaluated the performance and emission
impacts of ride-hailing service with three dispatching policies
and various EV penetration levels in the ride-hailing fleet. A
large-scale simulation platform was developed for the city of
San Francisco in SUMO to enable the application of ride-hailing,
electric vehicle charging, and idle vehicle
repositioning. Simulation results indicate that with a 60%
EVs in the simulated fleet, the off-peak EV priority policy and
off-peak EV only policy can reduce CO2 emissions by 32% -
40% while preserving the mobility performance in terms of
deadheading, total travel distance, and average rider pick-up
time. It is important for ride-hailing platforms to increase the
zero-emission rides and encourage ride pooling to comply
with California’s Clean Miles Standard.
more »
« less
- Award ID(s):
- 2152258
- PAR ID:
- 10511110
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7066-9
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Riverside, CA, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The emerging prevalence of electric vehicles (EVs) in shared mobility services has led to a groundbreaking trend for decarbonizing the shared mobility sector. However, it is still unclear how to maximize the efficiency of EVs to reduce greenhouse gas (GHG) emissions while maintaining high service quality, particularly considering the ongoing transition towards a fully electrified service fleet. In this paper, focusing on meal delivery, we proposed an eco-friendly on-demand meal delivery (ODMD) system to maximize the utilities of EVs to mitigate GHG emissions and maintain low operational cost and delay cost. The main feature of our system is that its fleet consists of electric and gasoline vehicles mirroring the evolving electrification trend in the shared delivery sector. A rolling horizon framework integrated with the adaptive large neighborhood search (RHALNS) algorithm was proposed to efficiently solve the meal order dispatching and routing problem with the mixed fleet. Three delivery policies were explored in the numerical study. Experiment results demonstrated that it is necessary for online meal delivery platforms to actively collect information of electric vehicles and take initiative to employ an eco-friendly delivery policy.more » « less
-
more » « less
Abstract Electric vehicle (EV) adoption promises potential air pollutant and greenhouse gas (GHG) reduction co‐benefits. As such, China has aggressively incentivized EV adoption, however much remains unknown with regard to EVs’ mitigation potential, including optimal vehicle type prioritization, power generation contingencies, effects of Clean Air regulations, and the ability of EVs to reduce acute impacts of extreme air quality events. Here, we present a suite of scenarios with a chemistry transport model that assess the potential co‐benefits of EVs during an extreme winter air quality event. We find that regardless of power generation source, heavy‐duty vehicle (HDV) electrification consistently improves air quality in terms of NO2and fine particulate matter (PM2.5), potentially avoiding 562 deaths due to acute pollutant exposure during the infamous January 2013 pollution episode (∼1% of total premature mortality). However, HDV electrification does not reduce GHG emissions without enhanced emission‐free electricity generation. In contrast, due to differing emission profiles, light‐duty vehicle (LDV) electrification in China consistently reduces GHG emissions (∼2 Mt CO2), but results in fewer air quality and human health improvements (145 avoided deaths). The calculated economic impacts for human health endpoints and CO2reductions for LDV electrification are nearly double those of HDV electrification in present‐day (155M vs. 87M US$), but are within ∼25% when enhanced emission‐free generation is used to power them. Overall, we find only a modest benefit for EVs to ameliorate severe wintertime pollution events, and that continued emission reductions in the power generation sector will have the greatest human health and economic benefits.
-
more » « less
Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region’s 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD.
-
more » « less
Abstract Ride-hailing can potentially provide a variety of benefits to individuals who need to chain several activities together within a single trip chain, relative to other travel modes. Using household travel diary/survey data, the goal of this study is to assess the role ride-hailing currently plays within trip chains. Specifically, the study aims to determine, within trip chains, who uses ride-hailing services, for what trip/activity purposes, and to/from what types of areas, as well as the characteristics of trip chains that involve ride-hailing segments. To meet these objectives, the study estimates a binary logit model using 2017 National Household Travel Survey data, where the dependent variable denotes the inclusion of at least one ride-hailing trip within a trip chain. Similar to the non-trip-chaining ride-hailing literature, this study indicates that trip chains with ride-hailing legs are positively associated with travelers who are younger, live in high-income households, frequently use transit, and reside in high-density areas. However, this study includes novel findings indicating statistically significant relationships between ride-hailing and trip chains that end in healthcare and social/recreational activities. Moreover, trip chains with ride-hailing tend to have fewer stops and longer activity durations than trip chains without ride-hailing. This study also includes nested logit choice models, wherein the dependent variable denotes the primary mode (ride-hailing, transit, personal vehicle, or non-motorized transport) of a trip chain. These model results provide additional insights into the role of ride-hailing within trip chains, as they allow for cross-mode comparisons. The paper discusses the potential transportation planning and policy implications of the model results as well as future research directions.
