The Impact of Bus Door Crowding on Operations and Safety

This study examines how bus design factors influence door crowding and quantifies how door crowding relates to operational performance and passenger safety. Results are based on data collected for 2,807 stops in Dhaka, Bangladesh. Door crowding is affected by multiple bus design factors, including door placement, aisle length, presence of a front seating area, and service type. Increases in door crowding are associated with longer marginal boarding times and an increased number of unsafe boarding and alighting movements that occur when the bus has not come to a complete stop. Results underscore the importance of educating conductors on the dangers associated with door crowding

Using Internet-based marketplaces to conduct surveys:an application to airline itinerary choice models

\u3cp\u3eWithin the transportation community, there has been increasing interest in using online outsourcing platforms such as Amazon Mechanical Turk (AMT) to conduct surveys. To date, transportation researchers’ use of AMT has been justified based on findings from studies in other fields. That is, to the best of our knowledge, there has been no study that has evaluated how the distribution of responses associated with each question and behavioral model estimated from AMT survey data compares to survey data collected from a traditional platform for a travel behavior application. This paper fills an important gap in the literature by examining (1) whether the distributions of responses from AMT and Qualtrics (a traditional market research firm) respondents are statistically equivalent, and (2) whether itinerary choice models estimated from these two surveys are statistically equivalent? Results show that AMT and Qualtrics respondents reported similar air trip characteristics and were drawn from a similar geographic distribution, but they exhibited distinct sociodemographic characteristics. After controlling for different age distributions in the two datasets, we found that airline itinerary choice models estimated from the AMT and Qualtrics survey data produced similar results, with the key difference related to price sensitivities. Our study provides preliminary evidence on the viability of using AMT and similar online outsourcing platforms for air travel behavior studies.\u3c/p\u3

Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data

We present a summary of important computational issues and opportunities that arise from the use of semi-aggregate data (where the explanatory data for choice scenarios are not necessarily unique for each decision-maker) in discrete choice models. These data are encountered with large transactional databases that have limited consumer information, a common feature in some transportation planning applications, such as airline itinerary choice modeling. We developed a freeware software package called Larch, written in Python and C++, to take advantage of these kind of data to greatly speed the estimation of discrete choice model parameters. Benchmarking experiments against Stata (a commonly used commercial package), Biogeme (a commonly used freeware package), and ALOGIT (a highly specialized commercial package for discrete choice modeling) based on an industry dataset for airline itinerary choice modeling applications shows that the size of the input estimation files are 50–100 times larger in Stata and Biogeme, respectively. Estimation times are also much faster in ALOGIT and Larch; e.g., for a small itinerary choice problem, a multinomial logit model estimated in ALOGIT or Larch converged in less than one second whereas the same model took almost 15 seconds in Stata and more than three minutes in Biogeme

Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data

We present a summary of important computational issues and opportunities that arise from the use of semi-aggregate data (where the explanatory data for choice scenarios are not necessarily unique for each decision-maker) in discrete choice models. These data are encountered with large transactional databases that have limited consumer information, a common feature in some transportation planning applications, such as airline itinerary choice modeling. We developed a freeware software package called Larch, written in Python and C++, to take advantage of these kind of data to greatly speed the estimation of discrete choice model parameters. Benchmarking experiments against Stata (a commonly used commercial package), Biogeme (a commonly used freeware package), and ALOGIT (a highly specialized commercial package for discrete choice modeling) based on an industry dataset for airline itinerary choice modeling applications shows that the size of the input estimation files are 50–100 times larger in Stata and Biogeme, respectively. Estimation times are also much faster in ALOGIT and Larch; e.g., for a small itinerary choice problem, a multinomial logit model estimated in ALOGIT or Larch converged in less than one second whereas the same model took almost 15 seconds in Stata and more than three minutes in Biogeme

Using Internet-based marketplaces to conduct surveys: an application to airline itinerary choice models

Within the transportation community, there has been increasing interest in using online outsourcing platforms such as Amazon Mechanical Turk (AMT) to conduct surveys. To date, transportation researchers’ use of AMT has been justified based on findings from studies in other fields. That is, to the best of our knowledge, there has been no study that has evaluated how the distribution of responses associated with each question and behavioral model estimated from AMT survey data compares to survey data collected from a traditional platform for a travel behavior application. This paper fills an important gap in the literature by examining (1) whether the distributions of responses from AMT and Qualtrics (a traditional market research firm) respondents are statistically equivalent, and (2) whether itinerary choice models estimated from these two surveys are statistically equivalent? Results show that AMT and Qualtrics respondents reported similar air trip characteristics and were drawn from a similar geographic distribution, but they exhibited distinct sociodemographic characteristics. After controlling for different age distributions in the two datasets, we found that airline itinerary choice models estimated from the AMT and Qualtrics survey data produced similar results, with the key difference related to price sensitivities. Our study provides preliminary evidence on the viability of using AMT and similar online outsourcing platforms for air travel behavior studies

A new twist on the gig economy: conducting surveys on Amazon Mechanical Turk

There is growing interest in using online outsourcing platforms that are part of the “gig economy” to conduct surveys for academic research. This interest has been driven in part by the belief that compared to traditional survey data collection methods, internet-based marketplaces such as Amazon Mechanical Turk (MTurk) enable one to collect survey data cheaper and faster from a larger, more diverse participant pool. However, many have questioned whether models based on survey data from these online marketplaces are similar to models based on survey data from more traditional platforms. To investigate this research question, we used MTurk and Qualtrics (a traditional market research firm) to survey air travelers. Our results showed that MTurk and Qualtrics respondents had distinct socio-demographic characteristics, but we found no statistical evidence for different air trip characteristics. In our data, proportionately more MTurk respondents were in the younger, single, male, and lower-income categories than for Qualtrics respondents. We found that airline itinerary choice models estimated from the MTurk and Qualtrics survey data were similar, with the key difference related to price sensitivities. Although our results provide evidence that MTurk can be used for travel demand modeling applications, we offer words of caution for others planning to conduct surveys in online marketplaces, particularly for those seeking to recruit more than 1000 participants or for those targeting specific geographic areas

A new twist on the gig economy: conducting surveys on Amazon Mechanical Turk

There is growing interest in using online outsourcing platforms that are part of the “gig economy” to conduct surveys for academic research. This interest has been driven in part by the belief that compared to traditional survey data collection methods, internet-based marketplaces such as Amazon Mechanical Turk (MTurk) enable one to collect survey data cheaper and faster from a larger, more diverse participant pool. However, many have questioned whether models based on survey data from these online marketplaces are similar to models based on survey data from more traditional platforms. To investigate this research question, we used MTurk and Qualtrics (a traditional market research firm) to survey air travelers. Our results showed that MTurk and Qualtrics respondents had distinct socio-demographic characteristics, but we found no statistical evidence for different air trip characteristics. In our data, proportionately more MTurk respondents were in the younger, single, male, and lower-income categories than for Qualtrics respondents. We found that airline itinerary choice models estimated from the MTurk and Qualtrics survey data were similar, with the key difference related to price sensitivities. Although our results provide evidence that MTurk can be used for travel demand modeling applications, we offer words of caution for others planning to conduct surveys in online marketplaces, particularly for those seeking to recruit more than 1000 participants or for those targeting specific geographic areas

A hazard model of US airline passengers' refund and exchange behavior

This study explores the use of discrete choice methods for airline passenger cancellation behavior. A discrete time proportional odds model with a prospective time scale is estimated based on the occurrence of cancellations (defined as refund and exchange events) in a sample of tickets provided by the Airline Reporting Corporation. Empirical results based on 2004 data from eight domestic US markets indicate that the intensity of the cancellation process is strongly influenced by both the time from ticket purchase and the time before flight departure. Higher cancellations rates are generally observed for recently purchased tickets, and for tickets whose associated flight departure dates are near. Cancellations rates are influenced by several other covariates, including departure day of week, market, and group size.

Estimation of Airline Itinerary Choice Models Using Disaggregate Ticket Data

peer reviewedAirline itinerary choice models support many multi-million dollar decisions, i.e., they are used to evaluate potential route schedules. Classic models suffer from major limitations, most notably they use average fare information but to not correct for price endogeneity. We use a novel database of airline tickets to estimate itinerary choice models using detailed fare data and compare these to classic itinerary choice models that use aggregate fare information but correct for price endogeneity