Chicago Transit Authority Research Articles

Assessing the Environmental, Economic, and Route Optimization Impacts of Implementing a Green Bus System in Urban Areas

The proliferation of e-bus has swept the globe. Many countries have begun the transformation of electric buses. Electric buses are embraced worldwide for their environmental and low-cost advantages. This work will help local governments establish a complete e-bus system.The first question mainly addresses the ecological impact that would be brought by the impact. In this work, the Ecological Impact Quantification Model (EIQM) is developed to address gaseous pollution including Greenhouse gas carbon dioxide, pollutants nitric oxide and PM2.5. The ecological effect enhancement with e-bus system is presented, and especially, the gaseous pollution caused by electricity production is approximated by the Monte Carlo simulation method. As a result, this team figures out that with the development of e-bus system, the average CO2 emission per person decreases, having a reduction of 11391g at first, and 14039g after five years.The second question mainly addresses the financial problem that the government needs to face. In this work, the Quantification of Financial Impact Model (QFIM) is developed. This team utilizes the differential equation to obtain the exact varying rate of diesel fuel and electricity fee, and finally determined the possible range of the government deficit. By synthesizing these data, this team discovers that the government could start reducing its deficit after 8 years of the transformation from diesel bus to e-bus, having deficit from $-88,412,180 to $-64,182,722.The third question addresses the 10-year plan for the selected city and 2 additional cities. This team develops Optimized Deployment Model based on Greedy Algorithm (ODGA) to optimize the transformation plan over 10 years of existing bus routes and the number of e-bus to be constructed per year. Monetary and time utility is considered and Greedy Algorithm is introduced to find out the best travelling route for e-buses. This team successfully figures out the best transformation plan of travelling routes and number of e-bus to be constructed per year to maximize the efficiency, including 5-min interval departure time and 867 buses are required to run the proposed route in Chicago. Finally, the team writes a piece of letter to Chicago Transit Authority and proposes the 10-year plan of e-bus system construction to guarantee the steady implementation of e-bus.

Robust path recommendations during public transit disruptions under demand uncertainty

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.

Electric Vehicle Scheduling Problem with Tour Combinations

Vehicle scheduling problem (VSP) has been studied since mid-90s, and yet the emerging electrification technology adoption requires a revisit to the problem since recharging event schedules need to be planned. The single depot electric vehicle scheduling problem (SDEVSP) is NP-hard, and large-scale instances cannot be solved to optimality in reasonable time. Hence, a heuristic solution approach was developed to address the problem. In this approach, we generate short tours that can be served by an electric vehicle (EV) and use a mixed-integer non-linear program to combine tour tuples that maximize EV utilization. The model was demonstrated on a hypothetical case study using a depot of the Chicago Transit Authority.

Transit Customer Acceptance of Automated Fare Collection Systems

The Chicago Transit Authority (CTA) inaugurated an Automated Fare Collection (AFC) system in Fall 1997. More than 1 million daily rides now utilize AFC to access CTA’s bus and rail service networks. While this represents about three-fourths of all CTA riders, many have stayed with cash or tokens (though the latter are now being phased out); monthly passes have been fully integrated with the farecard technology.This article provides a one-year perspective on customer reactions to, acceptance of, and problems with the new AFC system. It examines “before” and “after” shifts among the various fare media options available, and discusses major differences for bus and rail customers regarding ease of purchasing automated farecards and the resultant greater usage levels for rail as compared to bus. This article also reviews behavior in purchasing precoded, fixed-value farecards; buying variable-value farecards at automated vending machines (AVMs) located at rail stations; and recharging previously purchasedfarecards at those AVMs. Systemwide customer satisfaction surveys conducted in 1995 and 1997 found that satisfaction ratings, particularly among bus riders, significantly improved for several different fare-related service attributes. Transactions handled by rail station customer assistants, the CTA customer service hot line, and its AFC express unit desk, in dealing with customer questions/problems regarding the new AFC equipment, are also discussed. Typical weekday complaint levels related to AFC—especially those involving refund requests—are quite modest, but require sustained levels of courtesy and quick response.

Comparing the Efficiency of Public Transportation Subunits Using Data Envelopment Analysis

This article discusses the need for a performance measure that compares the efficiencies of subunits within a transportation organization, reflects the diversity of inputs and outputs, and is objective and consistent. The study presents a method for developing such a performance indicator, and illustrates its use with an application to the park-and-ride lots of the Chicago Transit Authority. The proposed method applies Data Envelopment Analysis supplemented by Stochastic Frontier Analysis to estimate efficiency scores for each subunit. The research demonstrates how the scores can provide objective and valid indicators of each subunit’s efficiency, while accounting for key goals and values of internal and external stakeholders. The scores can be practically applied by a transit agency to identify subunit inefficiencies, and, as demonstrated by several brief case studies, this information can be used as the basis for changes that will improve both subunit and system performance.

Ridership and Operations Visualization Engine: An Integrated Transit Performance and Passenger Journey Visualization Engine

Transit agencies collect a vast amount of data on vehicle positions, passenger loading and, increasingly, origin–destination flows. Collecting and synthesizing these data to support operations and planning are significant challenges and can be constrained by information silos within transit agencies. In this paper, an open-source bus performance and journey visualization dashboard, Ridership and Operations Visualization Engine, is presented, which integrates multiple disparate data sources into a flexible and iterative analysis tool. It differs from existing commercial products by including origin–destination flows along with standard performance metrics, and is designed to be adaptable and relevant to any transit agency. Two case studies are presented to demonstrate the functionality of the dashboard: planning transit priority infrastructure and evaluating network design changes. The dashboard was developed in partnership with Chicago Transit Authority and Massachusetts Bay Transportation Authority, and practical details from the installation and maintenance procedures are included for prospective users.

Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?

The COVID-19 pandemic has induced significant transit ridership losses worldwide. This paper conducts a quantitative analysis to reveal contributing factors to such losses, using data from the Chicago Transit Authority's bus and rail systems before and after the COVID-19 outbreak. It builds a sequential statistical modeling framework that integrates a Bayesian structural time-series model, a dynamics model, and a series of linear regression models, to fit the ridership loss with pandemic evolution and regulatory events, and to quantify how the impacts of those factors depend on socio-demographic characteristics. Results reveal that, for both bus and rail, remote learning/working answers for the majority of ridership loss, and their impacts depend highly on socio-demographic characteristics. Findings from this study cast insights into future evolution of transit ridership as well as recovery campaigns in the post-pandemic era.

Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework

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.

Comparison of Door-to-Door Transit Travel Time Estimation Using Schedules, Real-Time Vehicle Arrivals, and Smartcard Inference Methods

Estimating passengers’ door-to-door travel time, for journeys that combine walking and public transit, can be complex for large networks with many available path alternatives. Additional complications arise in tap-on only transit systems, where passengers alightings are not recorded. For one such system, the Chicago Transit Authority, this study compares three methods for estimating door-to-door travel time: assuming optimal path choice given scheduled service, as represented in the General Transit Feed Specification (GTFS); assuming optimal path choice given actually operated bus service, as recorded by automatic vehicle location systems; and using inferred path choices based on automated fare collection smartcard records, as processed with an origin-destination-interchange (ODX) inference algorithm. As expected, ODX-derived travel times are found to be longer than those derived from GTFS, indicating that purely schedule-based travel times underestimate the travel times that are actually available and experienced, which can be attributed primarily to suboptimal passenger route choice. These discrepancies additionally manifest in significant spatial variations, raising concerns about potential biases in travel time estimates that do not account for reliability. The findings bring about a more comprehensive understanding of the interactions between transit reliability and passenger behavior in transportation research. Furthermore, these discrepancies suggest areas of future research into the implications of systematic and behavioral assumptions implied by using conventional schedule-based travel time estimates.

Transit development and housing displacement: The case of the Chicago Red Line Extension

Chicago's South Side has long been characterized as a “transit desert” – an area with high transit inaccessibility and insufficient infrastructure to meet residents' needs (Jiao & Dillivan, 2013). Without adequate transit, residents cannot reach employment opportunities or regional amenities – contributing to economic, spatial, and social marginalization. The Chicago Transit Authority's proposed Red Line Extension (RLE) is designed to connect the city's far south side neighborhoods to Chicago's core. Given the scope of the RLE, 175 parcels have been chosen for demolition, meaning that a similar number of households face displacement to make room for the RLE right of way – which may have potentially negative consequences in realizing the subsequent benefits of improved transit access. In this article, we perform an ex-ante analysis of RLE induced displacement. Specifically, we: 1) predict potential location choices that transit displacees are most likely to choose; and 2) analyze these locations in relation to access to transit, amenities, employment, and housing affordability, among others. Within the context of transportation planning, ex-ante analysis is important because it can minimize unintended and negative consequences of transit-induced displacement – like decreased transit access and a loss of potential neighborhood improvements - by predicting potential relocation choices for displacees. Such predicted choices can help planners and decision-makers better understand the trade-offs for directly affected households and thereby allow planners and decision-makers to assist in relocation assistance that maximizes the benefits of the necessarily displaced.

TRANSIT AFFINITIES

AbstractMuch of the scholarship on poor Black urban communities focuses on social disorganization at the neighborhood level and how Blacks experience various institutional inequalities that impact their access to quality education and housing, jobs, and equitable public transportation. But Black social life is not a monolith of chaos, subjugation, and inequalities, nor is it confined to stationary neighborhoods. Black urban life is in fact vibrant, celebratory, and communal. Using two years of ethnographic observations on Chicago Transit Authority (CTA) buses and trains, we highlight the sociality of Black mobile experiences within Black spaces. Specifically, we examine how Blacks, while traveling into and through majority Black communities, form positive intraracial relationships that we refer to as Black transit affinities, which are a type of actively developed, temporal, meaningful interactions that take place on mobile systems. These transit affinities move beyond linked fate and solidarity but are actively formed and have four distinctive features, they are: 1) personal; 2) mutually engaged; 3) actively maintained although interrupted by stops on the bus or train; and, 4) particular to majority-minority areas of the city. These transit affinities are intraracial and were not observed, as defined, interracially or in majority White areas of the city. We do not argue that they are exclusive to Blacks but that they took place among Blacks in Black spaces that have often been ascribed a narrative of disorganization, violence, and social fragmentation.

Incorporating Product Choice into Transit Fare Policy Scenario Models

Customer fare product choices can affect both ridership and revenue, so they are strategically important for transit agencies. Nearly all major agencies offer choices between pay-per-use and pass products, and with each potential fare change, agencies face decisions about whether to modify pass “multiples”—the number of rides needed to “break even” on a pass purchase. However, the simple elasticity spreadsheet models often used to analyze the potential ridership and revenue impacts of fare changes make little or no adjustment for shifts in fare product choices. This paper reviews different options for incorporating product choice into fare policy scenario models, and it presents a ridership and revenue prediction procedure that combines a multinomial logit fare product choice model with the logic of an elasticity spreadsheet model. This combination facilitates evaluation of complex fare changes that are likely to alter fare product market shares while maintaining much of the flexibility and simplicity of a traditional spreadsheet model. Additionally, the proposed model uses only preexisting, revealed-preference automated fare collection data rather than requiring customer surveys. The proposed model is demonstrated using examples at the Chicago Transit Authority (CTA). The CTA experienced a large shift from passes to pay-per-use following a fare change in 2013, illustrating the potential value of accounting for fare product choices in fare scenario evaluation.

Uber Economics: Evaluating the Monetary and Travel Time Trade-Offs of Transportation Network Companies and Transit Service in Chicago, Illinois

The potential diversion of passengers from public transit to transportation network companies (TNCs) is attracting considerable attention in metropolitan regions. Despite this, relatively little microeconomic analysis has been made available to explore how service attributes affect choices between the services offered by TNCs and public transit. To fill this shortfall, this study evaluates prices and service levels for Lyft, Lyft Line, UberX, UberPool, and Chicago Transit Authority (CTA) services in Chicago. Analysis of 3,075 fares and estimated travel times for 620 trips in the 4- to 11-mile range shows TNCs tend to be relatively costly when expressed in relation to the additional amount spent per unit of time saved. The average traveler using these four TNC services, across the entire sample, spends the equivalent of $42–$108 per hour saved—well above the $14.95/hr. the U.S. Department of Transportation (U.S. DOT) recommends assigning to the average transit passenger when conducting analyses about the value of time. However, for travelers on business and those between locations poorly served by transit, including trips between neighborhoods with less transit service than the downtown district, the analysis shows a significant share of passengers will likely find TNCs cost-effective options based on the U.S. DOT standard. The approach taken illustrates how the mobility benefits and competitive issues associated with TNCs can be systematically evaluated by reviewing the price and travel time characteristics of each trip.

Sharing the ride: A paired-trip analysis of UberPool and Chicago Transit Authority services in Chicago, Illinois

Lyft Line and UberPool offer travelers lower prices than personal ride-hailing service in exchange for the ability of drivers to serve other passengers during the ride. The rising popularity of these ridesplitting services raises questions about the potential diversion of traffic from urban transit services. To help answer those questions, this study explores travel time and fare differences between UberPool and traditional transit service on 50 paired trips, between 3 and 11 miles long, in Chicago, Illinois.UberPool is found to reduce trip times by an average of 67.6% between neighborhoods, compared to just 13.7% to and from the central business district. Multiple regression analysis shows travel times on CTA and UberPool are similar for trips in which transit riders do not need to make transfers or walk long distances. Consumers save $1.29/minute when using UberPool on trips originating or terminating in the downtown district, compared to just $.38/minute on trips between neighborhoods. In both cases, the amount spent per minute saved exceeds the level the U.S. Department of Transportation recommends using when conducting analysis about the value transit users place on saving time.

Data-Driven Opportunities from an Account-Based Fare Payment System

The development of an account-based transit card has provided an opportunity for the Chicago Transit Authority (CTA) to examine transit usage differently. This new automated fare card/smart card system integrates many parts of a transit fare system, such as physical card, fare product, purchase information, and transit use location. The data are also integrated on a much longer timeframe than the previous generation transit card system. Increased data longevity allows the CTA to embark on a longitudinal study of transit card usage by separating cards in a priori groups. In a period between May 2016 and May 2017, a slight decrease in cards used in the system led to a small ridership drop, but a larger drop in ridership was found in passengers who used the system from one year to the next. This longitudinal application of passenger utilization of the transit network provides the agency with additional information that can be used to adjust fare policies and start marketing campaigns based on customer usages. This allows the agency to improve decision-making ability with greater insight on customer behavior.

Estimation of Passengers Left Behind by Trains in High-Frequency Transit Service Operating Near Capacity

Measuring rail system crowding is important to transit agencies. Crowding data has implications for safety, operations control, service planning, performance measurement, and customer information. This paper proposes a bi-level regression model that transit agencies can use to estimate the number of passengers left behind on a platform by high-frequency trains operating at capacity. Inputs to the model include the number of passenger arrivals between trains and train departure times, which are derived from automatic fare collection and vehicle location data. The data are used to calculate the proposed measure of cumulative capacity shortage, which is shown to have high correlation with the number of passengers left behind. A bi-level regression approach is introduced and applied to calibrate the model parameters based on manual counts of passengers left behind. A case study using data from the Chicago Transit Authority’s Blue Line demonstrates promising results, with an adjusted coefficient of determination of 0.81. The model could be used for post-hoc analysis of crowding performance or, in the context of real-time operations monitoring, for near-term predictions of passengers left behind.

Transporting Urban Inequality through Public Transit Designs & Systems

Four large, and often overflowing, dumpsters are situated at one of the more than dozen bus stops at the Chicago Transit Authority's (CTA) Red Line 95th Street/Dan Ryan train station. This station is on the city's far south side and the ridership on the buses that board and disembark there and the train is predominantly minority. On a warm or hot day, the smell of bus engines and dumpster contents fill the waiting areas. One 28–year–old Black male passenger (BMP) noted, as he stood at one of the nearly one dozen (no seating available) bus stops at the station, “In the summer it's really horrible because of the smells, flies, and bees.” He also added that as far as he could remember “they've [the bus stop dumpsters] been here my whole life” (June 2012). His experience at the south end of this train line, which also has a majority minority ridership, is starkly different from the waiting experiences on the far north end of the same line, Howard Street, where the ridership is diverse (with a large white ridership). The north end station is surrounded by shops and restaurants, more open waiting spaces, and places to sit to wait for buses that travel through the adjoining bus depot. There are no bus stop benches at the south end station, even though there are over a dozen buses that use that station's depot.

Travel time to Clinic but not Neighborhood Crime Rate is associated with Retention in Care among HIV-positive Patients

Abstract Background Retention in HIV care can be impacted by structural factors such as crime, poverty, and clinic accessibility. We aimed to determine whether crime rate, travel time, or travel distance to clinic were associated with retention in care or viral suppression (VS) among people living with HIV (PLWH) at the largest provider of HIV care on the South Side of Chicago. Methods Using publicly available data in the Chicago Open Data Portal, we geocoded patient home addresses and clinic location. We measured distance from patient home to clinic, and travel time from patient home to clinic using car and Chicago Transit Authority (CTA) public transportation. We further measured crime rate within a two block radius of the public transportation route to clinic. Retention was defined as >2 visits, 90 days apart within 12 months, and patients were classified into 3 groups: continuously retained (CR), intermittently retained (IR), or lost to follow-up (LTFU), i.e., no visit in the last 12-months. Kruskal–Wallis rank-sum with Dunns pairwise test was used to determine whether travel time, travel distance, and crime rate were associated with retention or viral suppression. Results 780 patients were included in the study. Of these 273 (35%) were CR, 392 (50%) were IR, and 115 (15%) were LTFU. Figure 1 shows maps with geocoded data. Median distance from clinic was 3.6 [2.1–5.6] miles among those CR, 3.9 [2.5–6.0] miles among the IR, and 3.9 [2.6–6.2] miles among those LTFU. Median travel time by CTA was 37.2 [31.8–53.0] mins for CR, 42.9 [33–53] mins for IR, and 42.9 [33–59.1] mins for LTFU; by car was 15.9 [9.6–33] mins for CR, 17.1 [11.8–24.6] mins for IR, and 17.5 [12.2–24.1] mins for LTFU. Crime rate was similar across all retention groups. Though no associations were statistically significant at P < 0.05, there was a trend toward shorter distance (P = 0.07) and shorter car travel time (P = 0.06) among CR vs. IR. There was also a trend toward lower neighborhood crime rates among those VS vs. those not VS (P = 0.07). Conclusion Retention in care was not impacted by residing in high crime neighborhoods in Chicago. PLWHA who lived farther from HIV clinic and had longer travel time showed a trend toward being more likely to be IR in care vs. CR, but there was no such association for VS. Travel time may impact patient likelihood to attend HIV care appointments, but not necessarily adherence to ART. Disclosures J. P. Ridgway, Gilead FOCUS: Grant Investigator, Grant recipient

Gap-based transit assignment algorithm with vehicle capacity constraints: Simulation-based implementation and large-scale application

This paper presents a gap-based solution method for the time-dependent transit assignment problem with vehicle capacity constraints. A two-level, simulation-based methodology is proposed, which finds the least cost hyperpaths at the upper level and performs the assignment of transit travelers on the hyperpaths at the lower level. The detailed simulation of travelers and vehicles at the lower level allows modelers to capture transit network complexities such as transfers/missed connections, receiving a seat/standing and boarding/being rejected to board. This ‘hard’ implementation of vehicle capacity constraints at the lower level is aggregated into ‘soft constraints’ at the upper level for the least cost hyperpath calculation. Using a gap-based assignment procedure, user equilibrium is reached on large-scale networks in a computationally efficient manner. The algorithm is tested on the large-scale Chicago Transit Authority network. The gap-based approach outperforms the commonly used method of successive averages approach in terms of rate of convergence and quality of results. Furthermore, sensitivity analyses with respect to network parameters illustrate the robustness of the proposed two-level solution procedure.

The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line

Transit agencies frequently upgrade rail tracks to bring the system to a state of good repair (SGR) and to improve the speed and reliability of urban rail transit service. For safety during construction, agencies establish slow zones in which trains must reduce speed. Slow zones create delays and schedule disruptions that result in customer dissatisfaction and discontinued use of transit, either temporarily or permanently. While transit agencies are understandably concerned about the possible negative effects of slow zones, empirical research has not specifically examined the relationship between slow zones and ridership. This paper partially fills that gap. Using data collected from the Chicago Transit Authority (CTA) Customer Experience Survey, CTA Slow Zone Maps, and, the Automatic Fare Collection System (AFC), it examines whether recurring service delays due to slow zones affect transit rider behavior and if the transit loyalty programs, such as smart card systems, increase or decrease rider defections. Findings suggest that slow zones increase headway deviation which reduces ridership. Smart card customers are more sensitive to slow zones as they are more likely to stop using transit as a result of delay. The findings of this paper have two major policy implications for transit agencies: (1) loyalty card users, often the most reliable source of revenue, are most at risk for defection during construction and (2) it is critical to minimize construction disruptions and delays in the long run by maintaining state of good repair. The results of this paper can likely be used as the basis for supporting immediate funding requests to bring the system to an acceptable state of good repair as well as stimulating ideas about funding reform for transit.