Intrapersonal variability in public transport path choice due to changes in service reliability

Uncertainty during the course of a trip is regarded as a particularly disturbing phenomenon by public transport (PT) passengers and has been shown to have a substantial impact on travel behaviour. However, so far there has been little empirical evidence within contemporary research concerning the degree of mid- to long-term adaptation among PT passengers to changes in the principal cause of such uncertainty within PT transport systems: Lack of service departure reliability. While most studies to date have focussed on instantaneous or short-term behavioural responses, this paper presents results from a one-year panel dataset consisting of individuals’ trip itineraries derived from smart card transactions enriched by automatic vehicle location (AVL) data. We measured long-term line route usage and departure reliability, the latter in terms of headway regularity and schedule adherence, in two panel waves. Thus, we aimed for practical indicators that may be associated with specific line routes and origin–destination PT stop pairs used for recurring trips over time. Results from logistic regression indicate a consistent significant impact of changed headway regularity and punctuality change for line routes with relatively high departure frequency. However, there is a relative indifference to long-term reliability change by a significant share of individuals that have actual trade-offs between at least two line route options, possibly indicating satisficing behaviour among PT passengers. Implications on PT path choice forecasting and route planning are discussed.

Big Data Insights into Container Vessel Dwell Times

Analysis of automatic identification system (AIS) vessel call records can greatly improve our understanding of container vessel dwell times when coupled with information on port volumes and vessel schedules. Port productivity discussions often use vessel time in port—referred to as dwell time, turnaround time, or berth time—as a primary metric. This emphasis implies a need to understand the factors that determine dwell time, especially in port comparisons. Previous dwell time analyses have been handicapped by limited data. This analysis differs in that it uses a multiyear, multiport database covering all relevant vessel calls at major continental U.S. container ports (Baltimore, Boston, Charleston, Houston, Jacksonville, Long Beach, Los Angeles, Miami, Mobile, New Orleans, Northwest Seaport Alliance, New York–New Jersey, Oakland, Palm Beach, Philadelphia, Port Everglades, Savannah, Virginia, and Wilmington, NC), and by including vessel schedules and seasonality. The analysis indicated a much stronger association of dwell time with expected cargo volume at each call than with vessel capacity, and expected cargo volumes helped explain port dwell time differences. The analysis also found that vessel schedules may be the primary determinants of dwell time, and that schedule adherence may thus be equally important as dwell time per se. Seasonality also affected container vessel dwell time, but that influence may be complex as both weather conditions and seasonal cargo peaks probably affect the outcomes. Promising avenues for future research lie in merging AIS vessel call records with other data sets that, unfortunately, may not yet exist or be accessible.

Improving schedule adherence based on dynamic signal control and speed guidance in connected bus system

Purpose The purpose of this paper is to develop a dynamic control method to improve bus schedule adherence under connected bus system. Design/methodology/approach The authors developed a dynamic programming model that optimally schedules the bus operating speed at road sections and multiple signal timing plans at intersections to improve bus schedule adherence. First, the bus route was partitioned into three types of sections: stop, road and intersection. Then, transit agencies can control buses in real time based on all collected information; i.e. control bus operating speed on road sections and adjust the signal timing plans through signal controllers to improve the schedule adherence in connected bus environment. Finally, bus punctuality at the downstream stop and the saturation degree deviations of intersections were selected as the evaluation criteria in optimizing signal control plans and bus speeds jointly. Findings An illustrative case study by using a bus rapid transit line in Jinan city was performed to verify the proposed model. It revealed that based on the proposed strategy, the objective value could be reduced by 73.7%, which indicated that the punctuality was highly improved but not to incur excessive congestion for other vehicular traffic. Originality/value In this paper, the authors applied speed guidance and the adjustment of the signal control plans for multiple cycles in advance to improve the scheduled stability; furthermore, the proposed control strategy can reduce the effect on private traffics to the utmost extend.

Development of the Optimization Models and Solution Algorithms for the Vessel Schedule Recovery Problem in Liner Shipping

Over 80% of the global trade tonnage and 70% of global trade value are carried by oceangoing vessels around the world. However, vessel routing and scheduling is a challenging exercise in liner shipping because of uncertainties that may affect the planned operations. Delays caused by uncertainties, such as weather, natural hazards, labor strikes, and others, increase the complexity of liner shipping, related to schedule adherence and reliability of service. In the event of a delay, liner shipping companies take decisions to recover the schedule, aiming to deliver cargoes in a timely manner. Vessel operators may decide to increase the sailing speed of the ship, which will further increase bunker fuel consumption and the total operational cost of liner shipping operations. The vessel schedule recovery problem becomes even more difficult if the delay is large. The contributions of this dissertation to the state-of-the-art are as follows: 1) a set of operational-level vessel schedule recovery models in liner shipping; 2) the vessel schedule recovery models for the liner shipping routes that pass through Emission Control Areas; 3) consideration of multiple vessel schedule recovery alternatives (e.g., port skipping with and without cargo diversion, speed adjustment, handling rate adjustment); 4) application of the appropriate solution approaches for solving the vessel schedule recovery problem; 5) identification of the key factors that may influence vessel schedule recovery; and 6) presentation of the managerial insights and benefits of the developed vessel schedule recovery optimization models.

Human Papillomavirus Vaccination Schedule: Adherence Among Commercially Insured Adolescents and Young Adults in the United States, 2011–2017

Objectives. To determine rates of human papillomavirus (HPV) vaccine adherence to the 2011 and 2016 Advisory Committee for Immunization Practices (ACIP) recommendations in the United States. Methods. We conducted a retrospective cohort study by using the 2011 to 2017 Marketscan data for beneficiaries aged 9 to 26 years who had at least 1 claim for HPV vaccination between January 1, 2011, and January 1, 2017. According to the 2011 ACIP recommendation, adherence is defined as 30 to 90 days between the first and second vaccination and 168 to 212 days between the first and third vaccination. According to the 2016 recommendation, preadolescents are classified as adherent if they had 2 claims of vaccination within 168 to 212 days. We calculated proportions of completion and adherence by recommendation. Results. Among patients classified under the 2011 ACIP recommendation (n = 2 164 096), 8.3% completed all 3 doses of the vaccine series. Of those who completed, 69.6% were considered adherent to the recommended schedule. Completion and adherence increased to 9.6% and 70.8%, respectively, among patients who were classified under the 2016 ACIP recommendation. Conclusions. Simpler recommendations lead to better adherence to the HPV vaccination schedule.

Travel Time Reliability Measures Accommodating Scheduling Preferences of Travelers

Existing travel time reliability measures fail to accommodate scheduling preferences of travelers and cannot distinguish between the variability associated with early and late arrivals. This study introduces two new travel time reliability measures based on concepts from behavioral economics. The first proposed measure is an indicator of the width of travel time distribution. It considers scheduling preferences of travelers and can distinguish between early arrival and late arrival. The second measure determines the skewness of travel time distribution. To estimate the proposed measures, travel time is modeled by mixture models and closed-form expressions are derived for the expected values of early and late arrivals. In addition, real travel time data from a freeway segment is used to compare the proposed measures with the existing travel time reliability measures. The results suggest that, although there exist significant correlations between travel time reliability measures, travelers’ preferences have considerable effects on the travel time reliability as perceived by them. Furthermore, four measures are developed based on the notions of early and late arrivals to assess the on-time performance (schedule adherence) of transit vehicles at stop level. The results of this study show that the four measures can serve as complementary to the existing on-time performance indices.