Search

When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate congestion during public transit disruptions. Passengers with different origins, destinations, and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation problem as an optimal flow problem with uncertain demand information. To tackle the lack of analytical formulation of travel times due to capacity constraints, we propose a simulation-based first-order approximation to transform the original problem into a linear program. Uncertainties in demand are modeled using robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (−20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce system travel times. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. The improvement of robust models is more prominent when the actual demand pattern is close to the worst-case demand. • A transit path recommendation model for minimizing system travel times under disruptions. • Using robust optimization to consider demand uncertainty. • Propose a simulation-based first-order approximation for the non-tractable objective function. • A case study in the Chicago public transit system with a real-world disruption. • The proposed model can reduce the incident-line passengers' travel time by 20%. [ABSTRACT FROM AUTHOR]

This study proposes a probabilistic framework to infer passengers' responses to unplanned urban rail service disruptions using smart card data in tap-in-only public transit systems. We first identify 19 possible response behaviors that passengers may have based on their decision-making times and locations (i.e, the stage of their trips when an incident happened), including transferring to a bus line, canceling trips, waiting, delaying departure time, etc. A probabilistic model is proposed to estimate the mean and variance of the number of passengers in each of the 19 behavior groups using passengers' smart card transactions. The 19 behavioral responses can be categorized from two aspects. From the behavioral aspect, they can be grouped into 5 aggregated response behaviors including using bus, using rail (changing or not changing route), not using public transit, and not being affected. The inference of the 19 behaviors can be classified into four cases based on the information used (historical trips vs. subsequent trips) and the context of the observed transactions (direct incident-related vs. indirect incident-related). The public transit system (bus and urban rail) of the Chicago Transit Authority (CTA) is used as a case study based on a real-world rail disruption incident. The model is applied with both synthetic data and real-world data. Results with synthetic data show that the proposed approach can estimate passengers' behavior well. The mean absolute percentage error (MAPE) for the estimated expected number of passengers in each behavior group is 20.5%, which outperforms the rule-based benchmark method (60.3%). The estimation results with real-world data are consistent with the incident's context. An indirect model validation method using demand change information and incident log data demonstrates the reasonableness of the results. • A probabilistic framework to infer passenger's response during disruption. • Estimate the mean and variance of the number of passengers with a specific behavior. • A case study in Chicago public transit systems using smart card data. • The probabilistic model significantly outperforms typical rule-based methods. • Results can help to improve public transit operating strategies during incidents. [ABSTRACT FROM AUTHOR]

• Investigated computational fairness in travel behavior modeling. • Operationalized computational fairness by equality of opportunity. • Revealed prediction disparity for race, income, gender, health and region. • Adopted a bias mitigation method to improve fairness in travel behavior prediction. • Demonstrated the accuracy-fairness tradeoff. Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. It highlights the accuracy-fairness tradeoff instead of the single dimensional focus on prediction accuracy in the contexts of deep neural network (DNN) and discrete choice models (DCM). We first operationalize computational fairness by equality of opportunity , then differentiate between the bias inherent in data and the bias introduced by modeling. The models inheriting the inherent biases can risk perpetuating the existing inequality in the data structure, and the biases in modeling can further exacerbate it. We then demonstrate the prediction disparities in travel behavior modeling using the 2017 National Household Travel Survey (NHTS) and the 2018–2019 My Daily Travel Survey in Chicago. Empirically, DNN and DCM reveal consistent prediction disparities across multiple social groups: both over-predict the false negative rate of frequent driving for the ethnic minorities, the low-income and the disabled populations, and falsely predict a higher travel burden of the socially disadvantaged groups and the rural populations than reality. Comparing DNN with DCM, we find that DNN can outperform DCM in prediction disparities because of DNN's smaller misspecification error. To mitigate prediction disparities, this study introduces an absolute correlation regularization method, which is evaluated with synthetic and real-world data. The results demonstrate the prevalence of prediction disparities in travel behavior modeling, and the disparities still persist regarding a variety of model specifics such as the number of DNN layers, batch size and weight initialization. Since these prediction disparities can exacerbate social inequity if prediction results without fairness adjustment are used for transportation policy making, we advocate for careful consideration of the fairness problem in travel behavior modeling, and the use of bias mitigation algorithms for fair transport decisions. [ABSTRACT FROM AUTHOR]

The bus control problem that combines holding and stop-skipping strategies is formulated as a multi-agent reinforcement learning (MARL) problem. Traditional MARL methods, designed for settings with joint action-taking, are incompatible with the asynchronous nature of at-stop control tasks. On the other hand, using a fully decentralized approach leads to environment non-stationarity, since the state transition of an individual agent may be distorted by the actions of other agents. To address it, we propose a design of the state and reward function that increases the observability of the impact of agents' actions during training. An event-based mesoscopic simulation model is built to train the agents. We evaluate the proposed approach in a case study with a complex route from the Chicago transit network. The proposed method is compared to a standard headway-based control and a policy trained with MARL but with no cooperative learning. The results show that the proposed method not only improves level of service but it is also more robust towards uncertainties in operations such as travel times and operator compliance with the recommended action. • A novel framework is proposed for joint holding and stop-skipping controls. • The framework is decentralized and considers nearby agents for evaluation. • Simulation experiments are conducted for a busy route operation in Chicago. • Metrics relevant to riders and agencies show the superiority of the proposed method. • The method shows robustness to reduced operator compliance. [ABSTRACT FROM AUTHOR]

To combat congestion, promote sustainable forms of transportation, and support the public transit system, Chicago introduced a congestion pricing policy targeting transportation network company (TNC) services on January 6, 2020. This policy aimed to discourage single-occupant and peak-period TNC travel, particularly in high-congestion areas. Using TNC trip record data collected from the Chicago Data Portal, we quantify the impacts of the congestion pricing policy on TNC ridership in Chicago, differentiating between shared and single-occupant trips. Employing a Difference-in-Differences identification strategy, we find that the implementation of the congestion pricing policy led to an increase in shared TNC trip counts and a much larger decrease in single-occupant trip counts. Overall, the policy implementation is associated with a 7.1% reduction of total TNC pickup trips, a 16.4% increase of shared TNC pickup trips and a 11% reduction of single TNC pickup trips. Given the estimated policy effects, we find that the price elasticity of the TNC trip volume in the downtown areas is roughly −0.48. In terms of spatial variation, we find that the lost TNC trips were mainly trips that began and ended in the central business district. The south side of Chicago, which has a high proportion of African-American and low-income residents, shows evidence of single trip reduction for trips that began or ended in the downtown areas due to the policy implementation, but the policy did not seem to incentivize pooling to or from the downtown areas as effectively in the south side as in other regions of Chicago. Regarding the time-of-day variation, we find that the policy is more effective in encouraging trip sharing for off-peak travels than for peak-time travels. Our research provides local planners and policymakers with valuable insights into the impacts of the congestion pricing policy. The method and findings of this research can also be used for other cities that are considering adopting congestion pricing policies on TNCs in the future. • We estimate the effect of a TNC congestion pricing policy on TNC ridership in Chicago. • A difference-in-differences identification strategy is adopted. • Results show 7.1% and 7.7% reductions of TNC pickup and dropoff trips, respectively. • The policy led to a growth in shared TNC trips and a decline in single TNC trips. • Results indicate a great spatial variation of the policy impacts. [ABSTRACT FROM AUTHOR]

As Transportation Network Companies (TNCs) have expanded their role in U.S. cities recently, their services (i.e. ridehailing) have been subject to scrutiny for displacing public transit (PT) ridership. Previous studies have attempted to classify the relationship between transit and TNCs, though analysis has been limited by a lack of granular TNC trip records, or has been conducted at aggregated scales. This study seeks to understand the TNC-PT relationship in Chicago at a spatially and temporally granular level by analyzing detailed individual trip records. An analysis framework is developed which enables TNC trips to be classified according to their potential relationship with transit: complementary (providing access to/from transit), substitutive (replacing a transit alternative), or independent (not desirably completable by transit). This framework is applied to both regular operating conditions and to early stages of the COVID-19 pandemic, to identify the TNC-PT relationship in these two contexts. We find that complementary TNC trips make up a small fraction of trips taken (approximately 2%), while potential independent trips represent 48% to 53% and potential substitution trips represent 45% to 50%. The percentage of substitution trips drops substantially following COVID-19 shutdowns (to around 14%). This may be attributed to a reduction in work-based TNC trips from Chicago's north side, indicated by changes in spatial distributions and flattening of trips occurring during peak hours. Furthermore, using spatial regression, we find that an increased tendency of TNC trips to substitute transit is related to a lower proportion of elderly people, greater proportion of peak-period TNC travel, greater transit network availability, a higher percentage of white population, and increased crime rates. Our findings identify spatial and temporal trends in the tendency to use TNC services in place of public transit, and thus have potential policy implications for transit management, such as spatially targeted service improvements and safety measures to reduce the possibility of public transit being substituted by TNC services. • We studied TNC-PT relationship at a spatially and temporally granular level. • TNC-PT relationship before and during the COVID-19 shutdown is examined. • Only approximately 2% of TNC trips complement public transit. • 45% to 50% of TNC trips substitute transit and this percentage drops during COVID-19 shutdowns. • Crime rate positively correlate with TNC-PT substitution both before and during COVID-19 shutdowns. [ABSTRACT FROM AUTHOR]