M Anil Yazici

A bus rapid transit line case study: Istanbul's metrobüs system

Implementation of Metrobus, the first bus rapid transit (BRT) line in Istanbul, Turkey, started in 2007. Since then, the line has been extended several times. After opening of the fourth phase in 2012, the BRT line will extend for 51.3 km. Currently, Metrobus carries around 600,000 passengers per day. It is the only intercontinental BRT system in the world. This paper describes Istanbuls Metrobus system features and usage and its reported benefits and costs. It also gives the reasons that underlie the positive public reception and the rapid ridership increase.

Challenges in Managing Centralized Taxi Dispatching at High-Volume Airports: Case Study of John F. Kennedy International Airport, New York City

This paper provides an evaluation of taxi dispatching procedures at New York Citys John F. Kennedy International Airport (JFK). Curbside data collection and interviews with airport stakeholders were conducted to describe and quantify conditions for taxi drivers and passengers at JFK. A literature review was performed to identify operational similarities and differences between JFK and other high-volume airports with centralized taxi dispatching and to identify potential solutions for application at JFK. The outcomes of this study include (a) characterization of relationships between airportwide and terminal-level passenger demands and available taxi supply at JFK, (b) identification of sources of inefficiency in existing taxi dispatching procedures and taxi operations, and (c) identification of approaches for addressing supply–demand imbalances and next steps in evaluating those approaches.

Analysis of Travel Time Reliability in New York City Based on Day-of-Week and Time-of-Day Periods

Travel time reliability in New York City was analyzed with three travel time reliability measures. A classification and regression tree model was used for the analysis. Instead of analysis of conventional peak and off-peak periods, day-of-week (DOW) and time-of-day (TOD) periods were determined on the basis of each travel time reliability measure. DOW and TOD periods were identified on the basis of average travel time and each selected measure. Travel time reliability measures formulated to explain the same phenomenon classified different periods as having similar characteristics. The results agreed with the literature that reliability measures should be based on temporal periods such as DOW and TOD; however, the selection of time periods should be measure specific. The impact of New York Citys urban grid network on travel time and speed distributions is also discussed. The travel time distribution patterns reported in the literature for freeways do not exist for the city. Therefore, caution is suggested for transferring reliability measures across different network structures.

Analysis of taxi demand and supply in New York City: implications of recent taxi regulations

This paper investigates temporal and weather-related variation in taxi trips in New York City. A taxi trip data-set with 147 million records covering 10 months of activity is used. It is shown that there are substantial variations in ridership, taxi supply, trip distance, and pickup frequency for different time periods and weather conditions. These variations, in turn, cause variations in driver revenues which is one of the main measures of taxi supply–demand equilibrium. The findings are then used to discuss the anticipated impacts of two recently enacted taxi regulation changes: the first fare increase since 2006 and the E-Hail pilot program which allows taxi hailing with smart phone applications. The fare increase is estimated to cause varying levels of revenue increase for different time periods. E-Hail apps are not expected to offer considerable improvements at all times, but rather when both adequate taxi supply and demand occur simultaneously.

Using Advanced Technologies to Manage Airport Taxicab Operations

Airports play a strategic role in economic development of cities and provide connectivity between business and commercial centers. Taxicab operations play a crucial role in determining the overall efficiency of an airports ground transportation system. However, airport-specific regulations pose challenges for managing taxicab operations effectively. The adoption of emerging technologies for managing taxicabs at airports offers faster, more efficient, and more cost-effective solutions to meet airport regulatory and operational requirements. In this case study, the operation of a central computerized taxicab dispatch system employed at John. F. Kennedy (JFK) International Airport in New York City is evaluated to identify observed limitations and associated avenues for improvement. Based on a review of state-of-practice taxicab dispatch systems deployed in airports worldwide, this research illustrates how modern technologies and procedural changes can be applied to optimize the taxicab operations at JFK and similar airports.

Highway versus urban roads: Analysis of travel time and variability patterns based on facility type

In this study, the differences in travel time variability patterns between urban roads and highways were analyzed. For urban roads, a GPS data set that included all taxi trips in New York City was used. For highways, automatic vehicle location data from the New Jersey Turnpike were used. The turnpike was divided into two sections, urban and suburban highway, according to urbanization level, time-of-day demands, and physical roadway features. The analysis not only compared travel time patterns for highways and urban roads but also investigated travel time characteristics along the same highway facility. First, the temporal variations of travel times on both facility types were calculated and compared. Second, the travel time distributions were extracted for various time periods and compared visually to determine the distributional patterns. Finally, the relationship between the average travel time and variability was investigated. Not only did travel time patterns differ between urban roads and highways, but major differences in travel time characteristics could be observed along the same highway. Higher travel times corresponded to lower reliability on highways yet corresponded to higher reliability on urban roads. Overall, the findings suggest that attributing differences in travel time variability patterns to facility type may be an oversimplification of the phenomenon.

Incident Detection Through Twitter

Traffic incident information is disseminated via Twitter from various types of accounts. It is more common to find active or transitive verbs and adverbs in tweets from personal accounts because individuals report personal experiences (e.g., “just saw an accident”). Tweets from an organization or agency [e.g., 511 (a telephone hotline for transportation information widely used in the United States and Canada), departments of transportation] are more structured and commonly include nouns and past participles (e.g., “one lane blocked”). Organization accounts mostly provide incident location, type, severity, and so on, whereas personal tweets do not usually provide such details. However, an agency tweets about an incident usually after the incident management (IM) officials have already been notified. Because of this timing, a personal tweet is more likely to carry useful “new” information for IM purposes. This study investigated the detection of traffic incidents through Twitter feeds by using these differences in structure and information content found in organization and personal social media accounts. Tweets collected via the Twitter public application programming interface were manually coded and treated separately as either personal or organizational, and the “dictionaries” used to perform relevancy classification were derived separately. Combinations of dictionaries (i.e., personal only, organizational only, personal and organizational) were used for “term frequency–inverse document frequency” and naïve Bayesian analysis. It was shown that analysis specific to account types helped achieve better accuracy in classification for targeting relevant tweets. Therefore, account-specific analysis should be considered for more efficient and effective event detection for IM purposes.

Feature selection for ranking of most influential variables for evacuation behavior modeling across disasters

The extensive list of factors that affect the evacuee decision process makes it difficult to design effective surveys and to develop decision models with high predictive power. Regression models and significance levels can help identify relevant variables and overcome this problem to an extent. However, such approaches fall short of ranking these variables or recognizing the redundant ones. In this study, the use of a feature selection method was proposed to ensure that the selected features were relevant and not at the same time redundant. This method, called conditional mutual information maximization, consists of picking features at each step and minimizes the uncertainty in the decision conditional on the response of any feature already picked. As a case study, the variables influencing evacuation behavior in the Northern New Jersey Evacuation Survey were ranked and compared for disaster scenarios. To validate the method and to demonstrate how it compared with the traditional methods, logistic regression models were also estimated with the same data set. It was found that the top-ranked variables might be available through an existing database such as the U.S. census and some could be calculated on the basis of the threat type and government action. This fact can be useful for emergency planners when an evacuation survey for a study area is not readily available. Overall, the feature selection algorithm succeeds in identifying the most influential factors for all threat types. The suggested approach can help both preprocessing (e.g., defining a set of input variables) and postprocessing (e.g., identification of variables that should be kept) for behavioral modeling.