Abstract
This project 1) develops a flexible mobility service model to accommodate different operational policies and strategies leveraging the real-world data (particularly for Columbus, OH); 2) develops a holistic multimodal transportation network modeling framework integrating mobility services with existing transportation modes; 3) assesses the system-level impacts of mobility services with different operational policies and strategies on the multimodal transportation network; and 4) simulates future mobility scenarios and analyzes their resulting effects on the system performance.
Description
Mobility service that picks up/drops off passengers between origins/destinations and main bus transit lines is recognized as a cost-effective solution to supplement the existing transportation modes by connecting travelers from low-density areas to regional transit network. This is generally referred to as first-mile last-mile (FMLM) service, with an extension to serve travelers with rider sharing, ride-hailing (through Transportation Network Companies, e.g. Uber Lyft), feeder transit. However, quantifying the system-level impact of mobility services can be challenging. First, mobility services may vary from area to area. Depending on different priorities and goals of the service providers, various operational policies and strategies may exist. For example, some agencies focus on coordinating mobility services with the mainline bus transit services to reduce transfer time while others emphasize tradeoffs among total user costs and travel time reliability. A flexible and general mobility service modeling framework in the real-world setting is needed to accommodate these different operation policies. Second, the multimodal transportation network is complex. Adding mobility services can affect people’s modal choices, and then alter traffic dynamics of the whole transportation network. Moreover, different operational policies of mobility services can also cause different system-level changes in modal share, system efficiency, environmental and societal impacts. There still lacks a quantitative method in the network context to evaluate the effectiveness of mobility services with different operational policies and strategies on improving transportation performance across multiple modes. Solving these challenges and thus providing affordable and reliable mobility services is especially important in low-density, car-centric American cities, such as Columbus, Ohio. The current car-centric design in these cities is unlikely to accommodate the sustainability needs of future suburban and population growth. For these reasons, Columbus provides a perfect test case to develop and evaluate solutions to resolve these challenges. To accomplish this, we propose to develop a quantitative modeling framework of evaluating the system-level impacts of mobility services on the multimodal transportation network. This framework will integrate emerging mobility services into the existing multimodal transportation network modeling and investigate the impacts of mobility services to driving and public transportation.
Task 1: Develop a flexible mobility service model to accommodate different operational policies and strategies
This mobility service model aims to simulate routing shuttles for rider pick-ups and drop-offs leveraging the real-world data (e.g., rider demand, travel time, and mainline bus schedule). To enable the model to be applicable to realistic and sizable network instances as in Columbus, we will develop efficient and effective algorithms for shuttle routing optimization. The performance and reliability of the model will be benchmarked against the existing mobility service data. Another feature of the model is its flexibility to incorporate different operational policies and strategies of the mobility service in the same framework. This can be useful when various policies and strategies may all exist across different areas or the service provider would like to evaluate the system-level impact of changing one policy to the other (e.g., increasing the number of shuttles, improving the coordination between mobility services and mainline bus services, and allowing for advanced reservation). This flexibility of the model paves the way for the assessment and comparison of these policies and strategies in the following tasks. This model can be developed based on the FMLM mobility service simulation and optimization model by Grahn et al. (2021).
To develop the sustainability metrics for Columbus, the following data will be obtained:
1. Traffic Analysis Zone (TAZ)
2. Roadway network
3. Data of existing mobility services in Columbus, OH (car sharing services, micro-transit services, and shared mobility services)
a. Origin and destination locations of rides
b. Origin and destination timestamps
c. Passengers riding on the shuttle/car
d. Passenger waiting times
The general first-mile last-mile (FMLM) service includes:
1. TNC trips
2. Micro-transit or feeder transit at selected communities
3. Micro-mobility at selected communities, e.g. Spin
Task 2: Develop a simulation tool to model traffic dynamics of the multimodal transportation network considering interactions between mobility services and other modes
The mobility service model developed in Task 1will be integrated into an existing multimodal transportation network modeling framework by Pi et al (2019). The current framework is limited to cars, trucks, and transit buses. With the added mobility services, travelers can change their modal choices, leading to altered traffic dynamics in the network. Utility functions of different travel modes will be established based on the literature and advanced routing and travel behavior algorithms will be employed to simulate the dynamic traffic flows throughout the regional multimodal network. Based on simulations, metrics regarding the system performance, such as roadway congestion, travel time, emissions, and mode choices can be evaluated.
The following types of mobility services will be included in the network modeling framework:
1. Carsharing service, e.g Zipcar
2. Transportation Network Companies, e.g. Uber/Lyft, (through Gridwise data)
3. Shared on-demand mobility service, e.g. SHARE
4. General first-mile last-mile (FMLM) service, including TNC trips, micro-transit or feeder transit at selected communities, micro-mobility at selected communities, e.g. Spin
Task 3: Assess the impacts of mobility services with different operational policies and strategies on the multimodal transportation network
Based on the multimodal transportation network modeling framework in Task 2, we can establish multiple scenarios corresponding to different mobility service operational policies and strategies (e.g., increasing number of shuttles and seat capacity, improving coordination between mobility services and mainline bus transit services, allowing for advanced reservation, and enabling demand prediction) and obtain the metrics reflecting the system performance from different perspectives, such as quality of public transit services and mobility services, roadway congestion, multi-modal options, air quality, accessibility, social equity, and energy consumption. By analyzing and comparing the metrics from different scenarios, we can derive implications of these policies and strategies, which may contribute to the better decision-making in mobility service operation and investment.
Timeline
Task 1: one month
Task 2: six months
Task 3: five months
We will also use the last month to complete a technical report and submit a manuscript to journal peer-reviews.
Strategic Description / RD&T
Deployment Plan
We will use the model to simulate traffic evolution in the roadway network as a result of various mobility service scenarios in the Columbus Metro network. This large-scale multi-modal network modeling and simulation framework is capable of quantifying the impact of mobility services in general to road traffic and evaluating management strategies about ridesharing. The output will provide the spatio-temporal distributions of both conventional travel modes and new mode of mobility services. We can also obtain the travel time, travel delay, vehicle-mile-traveled and emissions for each road segment and intersection by time of day for each travel mode. More importantly, it allows to quantify the impact to mobility, reliability, tolling revenue and equity, and trade off among those factors. Honda R&D will be our partner to jointly develop this modeling platform. We have been actively engaging deployment partners in Columbus Ohio, such as ODOT, COTA, SHARE. We plan to set up quarterly meetings with each of those partners to brief our research results, gauge their interests, receive their feedback and ultimately deploy our research outcomes in their day-to-day operations/policies.
Upon the completion of this project, we plan to actively seek both industrial and federal funding based on this initial development. Our framework is applicable to any large traffic networks with general mobility services. This generality will attract the attentions from various agencies and private mobility service providers. Potential funding agencies/collaborators include the Department of Transportation, Federal Highway Administration, National Science Foundation, National Institute of Standards and Technology, and local non-profits and companies.
Expected Outcomes/Impacts
We propose to develop a quantitative modeling framework of evaluating the system-level impacts of mobility services on the multimodal transportation network. This framework will integrate emerging mobility services into the existing multimodal transportation network modeling and investigate the impacts of mobility services to driving and public transportation. The assessment will base on a series of sustainability metrics established in the previous project (prior UTC projects), which quantifies system-level performance, such as efficiency, equity, accessibility, contributions to public health and environmental impacts. It will also compare the impacts of different operational polices and strategies of mobility services on the system performance. The findings are expected to provide more insights into implications of introduced micro-transit services and SHARE program in various Columbus communities and facilitate decision making in mobility service operation and investment optimization. The whole framework will be built on top of 1) the multimodal transportation network dynamic modeling techniques developed by Pi et al. (2019); 2) the FMLM mobility service simulation and optimization model by Grahn et al. (2021); and 3) the multi-modal network simulation and calibration platform established in the prior UTC project. This methodology can also be further generalized to other American cities with different policies and investments.
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| kfreymil@andrew.cmu.edu |
Freymiller, Kevin |
CEE |
Other |
Student - PhD |
| bethannh@andrew.cmu.edu |
Hockenberry, Beth |
CMU |
Other |
Staff - Business Manager |
| seanqian@cmu.edu |
Qian, Sean |
CEE |
PI |
Faculty - Untenured, Tenure Track |
Budget
Amount of UTC Funds Awarded
$100000.00
Total Project Budget (from all funding sources)
$220000.00
Documents
| Type |
Name |
Uploaded |
| Data Management Plan |
dmp_lB99OG2.docx |
March 2, 2022, 4:26 p.m. |
| Presentation |
A modeling framework to quantify impacts of mobility services |
Sept. 20, 2022, 8:21 p.m. |
| Progress Report |
384_Progress_Report_2022-09-30 |
Sept. 20, 2022, 8:21 p.m. |
| Publication |
2023_MMDODE_TRBAM.pdf |
March 14, 2023, 3:28 a.m. |
| Progress Report |
384_Progress_Report_2023-03-30 |
March 14, 2023, 3:28 a.m. |
| Publication |
Ridesharing and Digital Resilience for Urban Anomalies: Evidence from the New York City Taxi Market |
April 10, 2023, 8:23 p.m. |
| Publication |
How effective is reducing traffic speed for safer work zones? Methodology and a case study in Pennsylvania |
April 10, 2023, 8:23 p.m. |
| Publication |
Inferring truck activities using privacy-preserving truck trajectories data |
April 10, 2023, 8:24 p.m. |
| Publication |
The Impact of Optimized Fleets in Transportation Networks |
April 10, 2023, 8:25 p.m. |
| Publication |
Modeling the Impact of Tolling in Large-Scale Regional Networks: A Case Study for DVRPC |
April 10, 2023, 8:25 p.m. |
| Publication |
Modeling the Impact of Tolling in Large-scale Regional Networks: A Case Study for DVRPC [supporting dataset] |
April 10, 2023, 8:27 p.m. |
| Publication |
Optimal curbside pricing for managing ride-hailing pick-ups and drop-offs |
April 10, 2023, 8:28 p.m. |
| Publication |
Cluster analysis of day-to-day traffic data in networks |
April 10, 2023, 8:28 p.m. |
| Publication |
Special issue on dynamic transportation network modelling, emerging technologies, data analytics and methodology innovations |
April 10, 2023, 8:29 p.m. |
| Publication |
Evaluating Resilience in Mixed-Autonomy Transportation Systems [supporting dataset] |
April 10, 2023, 8:29 p.m. |
| Publication |
Evaluating Resilience in Mixed-Autonomy Transportation Systems |
April 10, 2023, 8:30 p.m. |
| Publication |
Optimizing first-and last-mile public transit services leveraging transportation network companies (TNC) |
April 10, 2023, 8:30 p.m. |
| Publication |
Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach |
April 10, 2023, 8:31 p.m. |
| Publication |
Statistical inference of travelers' route choice preferences with system-level data |
April 10, 2023, 8:32 p.m. |
| Publication |
Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs |
April 10, 2023, 8:32 p.m. |
| Publication |
Evaluating Resilience in Mixed-Autonomy |
April 10, 2023, 8:33 p.m. |
| Publication |
Identifying Temporal Instability in Factors Causing Work Zone Crash Occurrences Using Fast Causal Inference |
April 10, 2023, 8:33 p.m. |
| Publication |
A novel map-matching algorithm for relating work zones and crashes |
April 10, 2023, 8:34 p.m. |
| Final Report |
Final_Report_-_384.pdf |
Sept. 15, 2023, 10:45 a.m. |
Match Sources
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Partners
| Name |
Type |
| Ohio DOT |
Deployment & Equity Partner Deployment & Equity Partner |
| COTA |
Deployment & Equity Partner Deployment & Equity Partner |
