arXiv subjects

Susan Jia Xu

Publications and source records attributed to Susan Jia Xu.

3 recordsLinked to original sources

arXiv2021

Online route choice modeling for Mobility-as-a-Service networks with non-separable, congestible link capacity effects

With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of "congestible capacity"; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data from Citi Bike in New York City. The results show that the model can fit to the data quite well and performs better than a baseline modeling approach that ignores congestible capacity effects. By relating the route choice to congestible capacities using a random utility model, modelers can monitor and quantify the impacts to traveler consumer surplus in real time. Applications of the model and online method include monitoring capacity effects on consumer surplus, using the model to direct incentives programs for rebalancing and other revenue management strategies, and to guide resource allocation to mitigate consumer surplus impacts due to disruptions from incidents.

cs.CY↗

arXiv2020

Empirical validation of network learning with taxi GPS data from Wuhan, China

In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this "multi-agent inverse optimization" method using taxi GPS probe data from the city of Wuhan, China. Using a controlled 2062-link network environment and different GPS data processing algorithms, an online monitoring environment is simulated using the real data over a 4-hour period. Results show that using only samples from one OD pair, the multi-agent inverse optimization method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing to monitoring from just two OD pairs, the correlation improves further to 0.56.

physics.soc-ph↗

arXiv2017

Network learning via multi-agent inverse transportation problems

Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent modelers from fully exploiting ubiquitous data in monitoring transportation networks. Inverse optimization methods that capture network route choice behavior can address this gap, but they are designed to take observations of the same model to learn the parameters of that model, which is statistically inefficient (e.g. requires estimating population route and link flows). New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers' route behavior to infer shared network state parameters (e.g. link capacity dual prices). The inferred values are consistent with observations of each agent's optimization behavior. We prove that the method can obtain unique dual prices for a network shared by these agents in polynomial time. Four experiments are conducted. The first one, conducted on a 4-node network, verifies the methodology to obtain heterogeneous link cost parameters even when multinomial or mixed logit models would not be meaningfully estimated. The second is a parameter recovery test on the Nguyen-Dupuis network that shows that unique latent link capacity dual prices can be inferred using the proposed method. The third test on the same network demonstrates how a monitoring system in an online learning environment can be designed using this method. The last test demonstrates this learning on real data obtained from a freeway network in Queens, New York, using only real-time Google Maps queries.

cs.MA↗