Assessing the effects of a mixed-mode design in a longitudinal household travel survey

The German Mobility Panel (MOP) is a national household travel survey, which has been collecting data on travel behavior in Germany since 1994. One of the MOP’s central assets is its ability to provide time-series data on travel behavior. Thus, the comparability of survey results from different years is a major objective of the survey method used. Declining survey participation rates in the last decade in various socio-demographic groups resulted in the implementation of a mixed-mode design for the MOP in 2013, both for the sampling stage (landline and mobile phone recruitment) and the data collection stage (paper and web). In this study, we analyze whether the adaptations in the survey mode do indeed improve the results and, if so, why and to what degree. Ideally, the survey mode adaptions have increased the representativeness of the MOP. However, measurement biases due to the mixed-mode design are also conceivable. To decompose survey mode effects, we applied the propensity score weighting method. This method imputes the hypothetical responses participants would have given in different survey modes; disparities between actual responses and hypothetical responses under another mode are then traced back to the mixed-mode design. Our analysis indicates that trip-rate biases on shopping, leisure, and short trips are partly caused by the mixed-mode design; in contrast, quantities of time spent in the transportation system, trips made by car and public transportation, and commuting trips are hardly biased.

     

Mobile phone data in transportation research: methods for benchmarking against other data sources

The ubiquity of personal cellular phones in society has led to a surging interest in using Big Data generated by mobile phones in transport research. Studies have suggested that the vast amount of data could be used to estimate origin–destination (OD) matrices, thereby potentially replacing traditional data sources such as travel surveys. However, constructing OD matrices from mobile phone data (MPD) entails multiple challenges, and the lack of ground truth hampers the evaluation and validation of the estimated matrices. Furthermore, national laws may prohibit the distribution of MPD for research purposes, compelling researchers to work with pre-compiled OD matrices with no insight into the methods used. In this paper, we analyse a set of such pre-compiled OD matrices from the greater Oslo area and perform validation procedures against several sources to assess the quality and robustness of the OD matrices as well as their usefulness in transportation planning applications. We find that while the OD matrices correlate well with other sources at a low resolution, the reliability decreases when a finer level of detail is chosen, particularly when comparing shorter trips between neighbouring areas. Our results suggest that coarseness of data and privacy concerns restrict the usefulness of MPD in transport research in the case where OD matrices are pre-compiled by the operator.

     

Extent and correlates of walking in the USA

This paper examines data about walking trips in the US Department of Transportation’s 2001 National Household Travel Survey. The paper describes and critiques the methods used in the survey to collect data on walking. Using these data, we summarize the extent of walking, the duration and distance of walk trips, and variations in walking behavior according to geographic and socio-demographic factors. The results show that most Americans do not walk at all, but those who do average close to thirty minutes of walking a day. Walk trips averaged about a half-mile, but the median trip distance was a quarter of a mile. A significant percentage of the time Americans’ walk was spent traveling to and from transit trips. Binary logit models are used for examining utility and recreational walk trips and show a positive relationship between walking and population density for both. For recreational trips, this effect shows up at the extreme low and high ends of density. For utility trips, the odds of reporting a walk trip increase with each density category, but the effect is most pronounced at the highest density categories. At the highest densities, a large portion of the effect of density occurs via the intermediary of car ownership. Educational attainment has a strong effect on propensity to take walk trips, for both for utility and recreation. Higher income was associated with fewer utility walk trips but more recreational trips. Asians, Latinos, and blacks were less likely to take utility walk trips than whites, after controlling for income, education, density, and car ownership. The ethnic differences in walking are even larger for recreational trips.     

A decision support approach to the computerisation of some operational decision problems in community transport

Community Transport (CT) in the UK operates a diverse range of services, and organisations are computerising management and operational functions. This paper describes the approach which has been taken to computerising four operational decision making functions.

The paper considers models of human decision making and problem solving, with particular reference to an information processing view of cognitive activity and to perception and memory. The design of decision support systems is also discussed.

Four decision problems are considered. For each, the paper considers how people tackle the problem, how computers can be used to tackle it and the approach which has been adopted.

For allocating trips to vehicles using a diary, the approach has been to provide a representation on screen of a manual diary. For vehicle brokerage, vehicles are presented to the operator allocating a booking in an order based on the Sequence Number, an index of how ‘difficult to book’ a vehicle is, and the distance of the vehicle's base from the start point of the trip. For the sorting of passenger pick‐ups into an efficient tour, traditional solutions to the travelling salesperson problem have been rejected in favour of a solution using spacefilling curves. Finally, for allocating dial‐a‐ride passenger trips to vehicle shifts an approach has been chosen which presents the operator with appropriate information rather than attempting to automate the scheduling.

The paper concludes that the approach to the diary has been successful and accepted by operators, although the similar approach to the dial‐a‐ride scheduling has not, as the system has not yet been able to replace manual scheduling aids. The facility to order passenger pick‐ups is little used by operators. Finally, it is suggested that the vehicle brokerage problem may be an appropriate use of fuzzy logic.     

Does working from home reduce CO2 emissions? An analysis of travel patterns as dictated by workplaces

This research provides new evidence about the relationship between travel behavior, workplace diversification, and environmental impact in the United Kingdom using data from the National Travel Survey for the period between 2002 and 2017. The path analysis approach based on SEM handles both direct and indirect effects and allows for a comprehensive study of travel behavior, trade-off effects, and work and non-work trips. The results suggest that workplace diversification is often reflected by longer average distances for work trips, which are often associated with more remote residential locations. Findings also show that for some categories, such as teleworkers and home-based workers, trade-off effects are observed between work and non-work trips, which increase CO2 emission levels.     

The impact of transport pricing policy on individual energy consumption: a modeling case study in Kumamoto

To investigate the impact of traffic pricing policies on energy consumption, this study shows a microeconomic quantitative analysis scheme to simulate individual consumption behaviors from a microeconomic viewpoint. Energy consumption is estimated based on individual demand of non‐mobility goods and mobility goods under nine policy scenarios based on strategies of gasoline tax adding and mass transit fare reduction independently or combined. Results show that gasoline tax adding has strong effects on consumption behaviors. Energy consumption reduces mostly because of less consumption of non‐mobility goods and car trips. However, policy of mass transit fare reduction has limited impact on energy saving because consumption of non‐mobility goods and mass transit trips increases, but the number of car trips decline by only a small percentage. Comparing with single‐type policy, policies that combined gasoline tax adding and mass transit fare reduction show less energy consumption. Findings suggest that policies that increase cost of car trips, such as gasoline tax adding, are very helpful to reduce the consumption of non‐mobility goods and car trips, which contribute to less energy consumption. However, reducing cost of mass transit trips suggests limited effect on energy saving. Copyright © 2015 John Wiley & Sons, Ltd.     

Development of computerized vehicle scheduling system for specialized transportation: delaware case

A computer‐aided vehicle scheduling system is developed for Delaware's state‐wide specialized transportation system for elderly and handicapped persons. This paper presents the model, computer program package, system implementation, evaluation and the lessons learned from the project. The project which spanned two and a half years consists of five phases: (1) examine the manual method of vehicle scheduling (prior to the computerization); (2) develop a scheduling model; (3) develop a computer program package for the operation of the model; (4) install and operate the system; and (5) evaluate the system performance. The scheduling model consolidates passengers by block of time, origin and destination zones and along the direction of vehicle travel, first; second, minimizes the empty vehicle travel; and, lastly, assigns drivers to route considering assignment priority among the types of drivers and passengers. The product of the model is a set of driver log‐sheets which specify the sequence of passenger pick‐ups and drop‐offs. The computer program package includes necessary auxiliary data management functions such as registration of trip reservation, operating statistics and parameter value changes, as well as the execution of the model. It also allows the dispatcher to override the computer generated log‐sheet based on his discretion. The system has been operational for more than one year and it has brought about a number of changes in the operation and the role of the dispatcher. A before‐and‐after comparison of the operation and the lessons learned are also shown.     

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.     

Passengers’ response to transit fare change: an ex post appraisal using smart card data

Fare change is an effective tool for public transit demand management. An automatic fare collection system not only allows the implementation of complex fare policies, but also provides abundant data for impact analysis of fare change. This study proposes an assessment approach for analyzing the influence when substituting a flat-fare policy with a distance-based fare policy, using smart card data. The method can be used to analyze the impact of fare change on demand, riding distances, as well as price elasticity of demand at different time and distance intervals. Taking the fare change of Beijing Metro implemented in 2014 as a case study, we analyze the change of network demand at various levels, riding distances, and demand elasticity of different distances on weekdays and weekends, using the method established and the smart card data a week before and after the fare change. The policy implication of the fare change was also addressed. The results suggest that the fare change had a significant impact on overall demand, but not so much on riding distances. The greatest sensitivity to fare change is shown by weekend passengers, followed by passengers in the evening weekday peak time, while the morning weekday peak time passengers show little sensitivity. A great variety of passengers’ responses to fare change exists at station level because stations serve different types of land usage or generate trips with distinct purposes at different times. Rising fares can greatly increase revenue, and can shift trips to cycling and walking to a certain extent, but not so much as to mitigate overcrowding at morning peak times. The results are compared with those of the ex ante evaluation that used a stated preference survey, and the comparison illustrates that the price elasticity of demand extracted from the stated preference survey significantly exaggerates passengers’ responses to fare increase.     

Estimating time dependent O‐D trip tables during peak periods

Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real‐time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin‐destination (O‐D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O‐D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O‐D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding‐off, and, 2) low ratio of total number of trips to total number of O‐D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O‐D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed.     

Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands

In this paper, we introduce a new trip distribution model for destinations that are not homogeneously distributed. The model is a gravity model in which the spatial configuration of destinations is incorporated in the modeling process. The performance was tested on a survey with reported grocery shopping trips in the Dutch city of Almelo. The results show that the new model outperforms the traditional gravity model. It is also superior to the intervening opportunities model, because the distribution can be described as a function of travel costs, without increasing the computational time. In this study, the distribution was described by a simple function of Euclidean distance, which provides a good fit to the survey data. The slope of the distribution is quite steep. This shows that most trips are made to nearby supermarkets. However, a significant fraction of trips, mainly made by car, still goes to supermarkets further away. We argue that modeling of these trips by the new method will improve traffic flow predictions.     

Economic and service comparison of conventional bus and automated transit—a case study

Three origin‐destination matrices of inter‐zonal person trips for a section of the Los Angeles metropolitan region are analyzed using principal component analysis. The matrices represent total person trips, journey‐to‐work trips, and shopping trips. This allows for the identification of a number of sub‐regional travel fields or functional regions within the area. The composition of and interrelationships between these fields and the spatial coincidence of fields defined for different travel purposes are compared with existing and proposed public transit facilities.     

Applying a structured simulation-based methodology to assess carpooling time–space potential

Abstract

Increasing urban traffic congestion calls for the study of alternative measures. One such measure is carpooling, a system in which a person shares his private vehicle with one or more people in a commuter trip. In principle, this system could lead to potentially significant reductions in the use of private vehicles; however, in practice it has achieved limited success. In this paper, we apply a simulation-based methodology that uses aggregated data from commuter trips in an urban area to create compatible and feasible random trips. These are then analyzed through a heuristic process recursively to find grouping possibilities, thus producing indicators of carpooling potential such as the percentage of matched trips. Using this methodology, simulations are run for the Lisbon Metropolitan Area (Portugal) and results show that an increase in the number of participants in a carpooling scheme will only increase the probability of matching up to a certain point, and that this probability varies significantly with time–space trip attributes.     

Flow-based accessibility measurement: The Place Rank approach

In this paper, we empirically test the viability of a flow-based approach as an alternative to transport accessibility measurement. To track where commuters travel from and to (but not commute times), we use transactional smartcard data from residents in Singapore to construct the (daily) spatial network of trips generated. We use the Place Rank method to demonstrate the viability of the flow-based approach to study accessibility. We compute the Place Rank of each of 44 planning areas in Singapore. Interestingly, even though the spatial network is constructed using only origin–destination information, we find that the travel time of the trips out of each planning area generally decreases as the area’s Place Rank increases. The same is also the case for in-vehicle time, number of transfers in the network and transfer time. This shows that a flow-based approach can be used to measure the notion of accessibility, which is traditionally assessed using travel time information in the system. We also compare Place Rank with other indicators, namely, bus stop density, eigenvector centrality, clustering coefficient and typographical coefficient to evaluate an area’s accessibility. The results show that these indicators are not as effective as the Place Rank method.     

A new estimation approach for the multiple discrete–continuous probit (MDCP) choice model

This paper develops a blueprint (complete with matrix notation) to apply Bhat’s (2011) Maximum Approximate Composite Marginal Likelihood (MACML) inference approach for the estimation of cross-sectional as well as panel multiple discrete–continuous probit (MDCP) models. A simulation exercise is undertaken to evaluate the ability of the proposed approach to recover parameters from a cross-sectional MDCP model. The results show that the MACML approach does very well in recovering parameters, as well as appears to accurately capture the curvature of the Hessian of the log-likelihood function. The paper also demonstrates the application of the proposed approach through a study of individuals’ recreational (i.e., long distance leisure) choice among alternative destination locations and the number of trips to each recreational destination location, using data drawn from the 2004 to 2005 Michigan statewide household travel survey.     

The geographical stability of a typical trip production model: Applications of national and local data in four urban areas

Estimates of the numbers of trips likely to be made by individuals and of the modes of transport that will be available to them for those trips are provided by the trip production model. The objective of the work described in this paper was to investigate the geographical stability of the trip production model by comparing the numbers of trips estimated by the model when using national rather than local data. The 1972/3 National Travel Survey was used as the national data. Household interview survey data from the transportation studies of Lincoln, Sheffield/Rotherham, South East Dorset and Bristol were the local data sources. Three home based trip purposes are modelled; 24 hour work, 24 hour shop, 24 hour other.The models calibrated from national and local data perform similarly provided both operate with local trip rates. The car ownership sub-model with national parameters produces similar forecasts to the models with local parameters. There are probably real differences in household trip rates for some trip purposes between urban areas.     

Comparison on travel scheduling between driving and walking trips by habitual car users

Research on walking behavior has become increasingly more important in the field of transportation in the past decades. However, the study of the factors influencing the scheduling decisions related to walking trips and the exploration of the differences between travel modes has not been conducted yet. This paper presents a comparison of the scheduling and rescheduling decisions associated with car driving trips and walking trips by habitual car users using a data set collected in Valencia (Spain) in 2010. Bivariate probit models with sample selection are used to accommodate the influence of pre-planning on the decision to execute a travel as pre-planned or not. The explicative variables considered are: socio-economic characteristics of respondents, travel characteristics, and facets of the activity executed at origin and at destination including the scheduling decisions associated with them. The results demonstrate that a significant correlation exists between the choices of pre-planning and rescheduling for both types of trips. Whether for car driving or walking trips, the scheduling decisions associated with the activity at origin and at destination are the most important explicative factors of the trip scheduling and rescheduling decisions. However, the rescheduling of trips is mainly influenced by modifications in the activity at destination. Some interesting differences arise regarding the rescheduling decision processes between travel modes: if pre-planned, walking trips are less likely to be modified than car driving trips, showing a more rigid rescheduling behavior.     

GHG emissions in daily travel and long-distance travel in Germany – Social and spatial correlates

There is considerable research on the climate effects of daily travel, including research on the spatio-temporal and socioeconomic impact factors of daily travel and associated climate change effects. However, this is less true with respect to long-distance trips. This paper uses national transport survey data from Germany to point out differences in GHG emissions related to demographic, socioeconomic and spatial characteristics for daily and long-distance travel. Daily travel and long-distance travel are investigated simultaneously and separately using Logit and OLS regressions. The results show that transport-related GHG emissions from long-distance trips and daily trips are affected by sociodemographics in largely the same direction. In contrast, spatial attributes, like municipality size or density grade of the region, show a different picture. Per capita emissions in rural and suburban areas are higher for daily trips, but lower for long-distance trips than emissions caused by urban residents. While we cannot rule out the possibility of residential self-selection, our findings challenge the idea that compact urban development may help reduce CO₂ emissions once long-distance trips are taken into account.     

A measure of linked‐trip accessibility

The concept of accessibility has been variously interpreted as being the “nearness to places,” the “nearness to activities” and more recently the “ease of participating in activities.” With each of these qualitative interpretations, there has also been a variety of quantitative definitions of accessibility. This paper shows that many of the proposed definitions of accessibility can in fact be gathered together to form a spectrum of accessibility measures. These measures differ with respect to the factors included in their formulation and their degree of behavioural interpretation.

Existing measures of accessibility are shown to be deficient in one major aspect. That is, they assume that for any one measure of accessibility there is but one origin of trips. Thus, in estimating the accessibility of a point within a region it is assumed that all potential trips, which contribute to the accessibility of that point, start from that single point. In view of the considerable amount of evidence demonstrating the widespread, and increasing, occurrence of trip‐linking such a proposition must be viewed as being rather doubtful.

In the light of this, the paper proceeds to develop a measure of accessibility which explicitly accounts for the linking of trips. The implications of this measure, compared to a conventional unlinked‐trip accessibility measure, are discussed as are some problems which are foreseen in the practical implementation of such a measure.     

Passing Bays for Slow Moving Vehicles on Rural Two‐lane Roads

Abstract

Slow‐moving vehicles, including agricultural vehicles, on arterial highways can cause serious delays to other traffic as well as posing an extra safety risk. This paper elaborates on a small‐scale solution for these problems: the passing bay. It investigates the impacts of a passing bay on the total delay for other motorized vehicles, the number of passing manoeuvres and hindered vehicles, and the mean delay per hindered vehicle. The latter is also considered to be an indicator for traffic safety. The calculations are performed for two characteristic trips with a slow‐moving vehicle. The passing bay is an effective solution to reducing delays on arterial highways when two‐way hourly volumes exceed 600–1000 vehicles. The effects depend on the trip length and speed of the slow‐moving vehicle, and on the passing sight distance limitations of the road. A distance of 2–4?km between the passing bays seems an acceptable compromise between the reduction of delay for other motorized vehicles and the extra discomfort and delay for drivers of slow‐moving vehicles. This result also shows that passing bays are not effective in regions where slow‐moving vehicles mainly make trips shorter than this distance.