Urban bus companies have collected a tremendous amount of travel data from passengers in the past years. In spite of great value for bus schedule optimization, these data have not been fully exploited. In this paper, we leverage hundreds of millions of bus transaction records, generated when passengers board and alight, to infer time-dependent traffic and customer demand. When the traffic and demand information are available, we build an optimal model to schedule the departure time of each bus service with the objective of minimizing the average waiting time. Experimental results show that compared with the existing bus scheduling system, our model can help reduce the waiting time by a wide margin.

A Data-Driven and Optimal Bus Scheduling Model With Time-Dependent Traffic and Demand

Perceived travel time in public transport trip directly affects passengers’ satisfaction and therefore is an essential consideration when planning and operating the public transport system. However, beyond the prevalent analysis on the waiting time perception, there are few articles that have studied the travel time perception along the entire multimodal trip. In this context, this paper presents an empirical investigation of actual and perceived travel time at each stage in a bus-rail transport trip, where first mile, in-vehicle stage, transfer stage and last mile are considered. Data on actual and perceived travel time, socioeconomic characteristics, trip characteristics and facility usage are collected by accompanied survey undertaken from passengers’ originations to destinations. The results from a series of paired T-tests show that passenger do perceive travel time to be greater than the actual amount at each stage. Three linear regression models are developed for estimation of perceived walking, waiting and in-vehicle time. Results indicate that socioeconomic characteristics, trip characteristics and facility usage seem to have an impact on passengers’ travel time perception, while the travel time spent on the previous stage does not affect the perception much.

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Public transport travel time perception: Effects of socioeconomic characteristics, trip characteristics and facility usage

In proof-of-payment systems, fare evasion represents a crucial topic for public transport companies (PTCs) due to lost fare revenues, damaged corporate image, and increased levels of violence on public transport, which might also have negative economic repercussions on PTCs. Therefore, there is a need to establish the level of inspection (i.e. the number of inspectors) to tackle fare dodgers as a possible option. By building on previous models, this paper develops a formal economic framework to derive the optimum inspection level in a long time window, based on system-wide profit maximization when fare evasion exists. The framework takes into account: (i) the refined segmentation of passengers and potential fare evaders, (ii) the variability of perceived inspection level by passengers, and (iii) the fact that an inspector cannot fine every passenger caught evading. Its implementation is illustrated by using three years of real data from an Italian PTC. Based on 27,514 stop-level inspections and 10,586 on-board personal interviews, the results show that the optimum inspection level is 3.8%. Put differently, it is sufficient to check 38 passengers out of every 1000 to maximize profit in the presence of fare evasion. This outcome is very useful, because it improves the one obtained in previous formulations. Indeed, profit maximization is achieved with a lower number of inspectors, thus reducing inspection costs, which are relevant determinants in proficient PTCs. Finally, the framework is flexible and may be applied to public transport modes other than buses as long as proof-of-payment systems are in use.

Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level

Fare evasion has become an important issue for public transport companies, especially for those that have adopted proof-of-payment ticketing systems. Recent years have seen strong growth in the publication of studies on fare evasion. This paper reviews 113 studies to identify the characteristics of the research on fare evasion. An overview and classification in five main areas, i.e., fare evader-oriented, criminological, economic, technological, and operational is provided. Next, the status quo of these studies is assessed to support possible unifying research development.

Fare evasion in public transport systems: a review of the literature

Fare evasion is a problem in many public transport systems around the world and policies to reduce it are generally aimed at improving control and increasing fines. We use an econometric approach to attempt explaining the high levels of evasion in Santiago, Chile, and guide public policy formulation to reduce this problem. In particular, a negative binomial count regression model allowed us to find that fare evasion rates on buses increase as: (i) more people board (or alight) at a given bus door, (ii) more passengers board by a rear door, (iii) buses have higher occupancy levels (and more doors) and (iv) passengers experience longer headways. By controlling these variables (ceteris paribus), results indicate that evasion is greater during the afternoon and evening, but it is not clear that it is higher during peak hours. Regarding socioeconomic variables, we found that fare evasion at bus stops located in higher income areas (municipalities) is significantly lower than in more deprived areas. Finally, based on our results we identified five main methods to address evasion as alternatives to more dedicated fine enforcement or increased inspection; (i) increasing the bus fleet, (ii) improving the bus headway regularity, (iii) implementing off-board payment stations, (iv) changing the payment system on board and (v) changing the bus design (number of doors or capacity). Our model provides a powerful tool to predict the reduction of fare evasion due to the implementation of some of these five operational strategies, and can be applied to other bus public transport systems.

What is behind fare evasion in urban bus systems? An econometric approach

Nowadays, in proof-of-payment transit systems, fare evasion is provoking strong interest in public transport companies (PTCs) due to the relevant economic losses, social inequity and increased levels of violence affecting personal security. Therefore, there is the need to recognize possible fare evaders. By using 2177 on-board personal interviews, gathered from an Italian PTC, and logistic regression models, we isolate determinants of possible free-rider passengers and, hence, those whom it might be advisable to target in order to capitalize on the effect of the application of countermeasures on fare evasion put forward by the local PTC. We show that males, younger than 26 years, with a low education level, unemployed and/or students and without an alternative mode of transport other than the bus are more probably fare evaders. Moreover, people who make trips of shorter than 15 min, who are systematic users and are not satisfied with the service are possible fare evaders. Finally, we found that a low level of inspection, knowledge of fines and previous ticket violations are determinants which make people more prone to evade fares. These outcomes are very useful, because, to the best of our knowledge, they represent the first empirical contribution showing the determinants which help evaluate the propensity to be a fare evader, in probabilistic terms. Moreover, they could help PTCs understand who might be a fare evader, in order to anticipate suitable countermeasures.

What are the determinants in making people free riders in proof-of-payment transit systems? Evidence from Italy

Fare evasion is a major problem for transit companies due to lost fare revenues and damage to their corporate images. Therefore, the establishment and proper management of ticket inspection teams deployed to tackle fare dodgers is highly important and represents a severe challenge. In this paper, an existent profit maximization model for estimating the optimum level of inspection has been extended, calibrated, and tested in a real case, using data available from an Italian transit operator, resulting from 98 days of checks and 3659 completed on-board interviews. Given the current network-wide inspection level per single verifier, and considering the level of fines currently applied, the optimal value of the total inspection rate is found to amount to 4.5%. The model provides empirical evidence towards understanding the fare evasion problem, besides highlighting the need for collaboration with the managers of the transit company. An overview of the manipulation of some control variables related to risk perception and the main implications of the findings are presented to transport companies using “honour” ticketing systems.

A modified model to curb fare evasion and enforce compliance: Empirical evidence and implications

In proof-of-payment transit systems, fare evasion represents a crucial topic and undermines financial viability for public transport companies (PTCs). Two studies segmented all transit passengers by using qualitative research and explorative two-step cluster analysis as well as web-survey data. However, as far as the authors know, no study segmented exclusively the fare evader passengers, with the aim to know deeply characteristics of distinct groups, and to use data gathered from an intercept survey. Moreover, the combined use of a factorial and a cluster analysis was not adopted to solve this issue. By using 850 on-board personal interviews, gathered from an Italian PTC, a Multiple Correspondence Analysis and a Hierarchical Cluster Analysis have been performed to group the fare evader passengers. Three clusters of evaders’ passengers are derived: 1) the chronic, 2) the calculated-risk and 3) the accidental fare evader. The chronic cluster includes passengers who always evade the fare, and represents a medium numerous groups of fare evaders. The calculated-risk cluster includes passengers who usually evade the fare and represents the most numerous group of evaders. The accidental cluster includes passengers who rarely evades the fare and represents the less numerous groups. These outcomes are very relevant and useful for both research and practice. Indeed, this paper contributes to the empirical understanding of the determinants of being a fare evader for a selected group. Moreover, it helps PTCs understand how evaders' groups differ each other, adopt suitable strategies for each group and evaluate their effects.

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Segmenting fare evader groups by factor and cluster analysis

A strong interest in fare evasion is currently emerging in all proof-of-payment transit systems owing to its severe implications. Recent research has investigated from the passenger perspe...

Bruno Useli

Papers

Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level

What are the determinants in making people free riders in proof-of-payment transit systems? Evidence from Italy

A modified model to curb fare evasion and enforce compliance: Empirical evidence and implications

Segmenting fare evader groups by factor and cluster analysis

Assessing the Intention to Evade Fares for Demographic Segments of Passengers: Empirical Research in Italy for Building Smart(er) Cities