Non RU Publicationshttp://repository.ubn.ru.nl:80/handle/2066/913832015-05-25T00:02:54Z2015-05-25T00:02:54ZBeinvloedbaarheid van vervoerwijzekeuze in Den HaagMeurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1406212015-05-13T17:01:03Z2015-05-12T21:14:19ZBeinvloedbaarheid van vervoerwijzekeuze in Den Haag
Article in monograph or in proceedings
Meurs, H.J.
2015-05-12T21:14:19ZDynamic estimation of public transport demand elasticitiesMeurs, H.J.Eijk, T. vanGoodwin, P.http://repository.ubn.ru.nl:80/handle/2066/1406192015-05-19T06:42:22Z2015-05-12T21:14:18ZDynamic estimation of public transport demand elasticities
Article in monograph or in proceedings
Meurs, H.J.; Eijk, T. van; Goodwin, P.
2015-05-12T21:14:18ZBoek 10 BW. Een grote stap in de codificatie van het internationaal privaatrecht. Achtergronden en enige kanttekeningenStruycken, A.V.M.http://repository.ubn.ru.nl:80/handle/2066/1406092015-05-20T22:27:51Z2015-05-12T21:13:18ZBoek 10 BW. Een grote stap in de codificatie van het internationaal privaatrecht. Achtergronden en enige kanttekeningen
Article / Letter to editor
Struycken, A.V.M.
2015-05-12T21:13:18ZSome principles of dynamic analysis of travel behaviourGoodwin, P.Kitamura, R.Meurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1406052015-05-19T09:49:31Z2015-05-12T21:13:16ZSome principles of dynamic analysis of travel behaviour
Article in monograph or in proceedings
Goodwin, P.; Kitamura, R.; Meurs, H.J.
2015-05-12T21:13:16ZA constant travel time budget? In search for explanations for an increase in average travel.Wee, B. vanRietveld, P.Meurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1405972015-05-19T00:01:22Z2015-05-12T21:13:12ZA constant travel time budget? In search for explanations for an increase in average travel.
Article in monograph or in proceedings
Wee, B. van; Rietveld, P.; Meurs, H.J.
Recent research suggests that during the past decades the average travel time of the Dutch population has probably increased. However, different data sources show different levels of increase. Possible causes of the increase in average travel time are presented here. Increased incomes have probably resulted in an increase in both costs and benefits of travel. The increase in travel time may also be due to bene% having increased more rapidly than cos& Costs may even have decreased due to the increased comfort leve1 of cars and increased opportunities offered to make double use of one’s time (e.g. working in a train).
2015-05-12T21:13:12ZRecent research suggests that during the past decades the average travel time of the Dutch population has probably increased. However, different data sources show different levels of increase. Possible causes of the increase in average travel time are presented here. Increased incomes have probably resulted in an increase in both costs and benefits of travel. The increase in travel time may also be due to bene% having increased more rapidly than cos& Costs may even have decreased due to the increased comfort leve1 of cars and increased opportunities offered to make double use of one’s time (e.g. working in a train).Marktkansen teletaxi vooralsnog beperktMeurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1405932015-05-18T13:26:55Z2015-05-11T22:02:23ZMarktkansen teletaxi vooralsnog beperkt
Article / Letter to editor
Meurs, H.J.
2015-05-11T22:02:23ZSpecial issue: Land Use and Sustainable Mobility. EditorialMeurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1405922015-05-20T00:02:36Z2015-05-11T22:02:23ZSpecial issue: Land Use and Sustainable Mobility. Editorial
Article / Letter to editor
Meurs, H.J.
2015-05-11T22:02:23ZHet mobiliteitspanelMeurs, H.J.Wissen, L.J.G. vanhttp://repository.ubn.ru.nl:80/handle/2066/1405862015-05-19T08:37:48Z2015-05-11T22:02:20ZHet mobiliteitspanel
Article / Letter to editor
Meurs, H.J.; Wissen, L.J.G. van
2015-05-11T22:02:20ZBiases in response over time in a seven-day travel diaryGolob, T.F.Meurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1404792015-05-19T00:01:40Z2015-05-04T21:02:35ZBiases in response over time in a seven-day travel diary
Article / Letter to editor
Golob, T.F.; Meurs, H.J.
Data from multi-day travel or activity diaries might be biased if recording inaccuracies and tendencies for respondents to skip certain types of trips or activities increases (or decreases) from day-to-day over the diary period. One objective of the research reported here is to test for such temporal biases in a seven-day travel diary. A second objective is to calculate correction factors which can be applied to the data in the case that biases are found. The analyses were conducted using regression and analysis-of-variance techniques. The variables investigated included total trips per day, total travel time per day, and trips per day by various modes (such as walking, car driver and car passenger). Results showed that most biases per capita statistics are due to increases over time in the percentage of respondents reporting no travel at all for an entire day. However, even after accounting for this bias by measuring statistics in terms of “per mobile person”, there remains a decrease over time of about 3.5 percent per day in the reporting of walking trips. This appears to be the main factor in the overall bias of about one percent per day in total trips per mobile person per day. No significant differences were found among population segments in terms of the levels of their biases.
2015-05-04T21:02:35ZData from multi-day travel or activity diaries might be biased if recording inaccuracies and tendencies for respondents to skip certain types of trips or activities increases (or decreases) from day-to-day over the diary period. One objective of the research reported here is to test for such temporal biases in a seven-day travel diary. A second objective is to calculate correction factors which can be applied to the data in the case that biases are found. The analyses were conducted using regression and analysis-of-variance techniques. The variables investigated included total trips per day, total travel time per day, and trips per day by various modes (such as walking, car driver and car passenger). Results showed that most biases per capita statistics are due to increases over time in the percentage of respondents reporting no travel at all for an entire day. However, even after accounting for this bias by measuring statistics in terms of “per mobile person”, there remains a decrease over time of about 3.5 percent per day in the reporting of walking trips. This appears to be the main factor in the overall bias of about one percent per day in total trips per mobile person per day. No significant differences were found among population segments in terms of the levels of their biases.Trip generation models with permanent unobserved effectsMeurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1404772015-05-20T00:03:09Z2015-05-04T21:02:35ZTrip generation models with permanent unobserved effects
Article / Letter to editor
Meurs, H.J.
The objective of this paper is to examine whether the use of conventional trip generation models based on cross-sectional data will produce biased results. Panel data are used to control for omitted time invariant household effects. The methodology is based upon fixed and random effects models. The results indicate that cross-sectional models for total tripmaking, transit and car usage may lead to seriously misleading results if used to assess the effects of changes in the travel environment. The methodology seems to provide a proper way of taking unobserved heterogeneity into account. The difference in the results between fixed and random effects models may be the result of correlation between the omitted and included explanatory variables. A test for measurement error in the explanatory variables suggests that the results will not be significantly affected by this problem.
2015-05-04T21:02:35ZThe objective of this paper is to examine whether the use of conventional trip generation models based on cross-sectional data will produce biased results. Panel data are used to control for omitted time invariant household effects. The methodology is based upon fixed and random effects models. The results indicate that cross-sectional models for total tripmaking, transit and car usage may lead to seriously misleading results if used to assess the effects of changes in the travel environment. The methodology seems to provide a proper way of taking unobserved heterogeneity into account. The difference in the results between fixed and random effects models may be the result of correlation between the omitted and included explanatory variables. A test for measurement error in the explanatory variables suggests that the results will not be significantly affected by this problem.A structural model of temporal change in multi-modal travel demandGolob, T.F.Meurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1404782015-05-20T00:02:07Z2015-05-04T21:02:35ZA structural model of temporal change in multi-modal travel demand
Article / Letter to editor
Golob, T.F.; Meurs, H.J.
A simultaneous equation model is developed to describe temporal trends and shifts in demand among five modes of passenger transportation in the Netherlands. The modes are car driver, car passenger, train, bicycle, and public transit (bus, tram, and subway). The time period is one year (1984–1985). The data are from the week-long travel diaries at six-month intervals of a national panel of households in the Netherlands. The model explains the weekly trip rates for each mode in terms of three types of relationships: links from demand for the same mode at previous points in time (temporal stability or inertia); links to and from demand for other modes at the same point in time (complementarity and competition on a synchronous basis); and links from demand for other modes at previous points in time (substitution effects). a significant model is found with 15 inertial links, 21 synchronous links, and 16 cross-lag links among the variables. It is proposed in interpretations of the link coefficients and overall effects of one variable on another that relationships among the modes are evolving over time. In particular, the model captures the effect of a public transit fare increase that occurred during the time frame of the panel data.
2015-05-04T21:02:35ZA simultaneous equation model is developed to describe temporal trends and shifts in demand among five modes of passenger transportation in the Netherlands. The modes are car driver, car passenger, train, bicycle, and public transit (bus, tram, and subway). The time period is one year (1984–1985). The data are from the week-long travel diaries at six-month intervals of a national panel of households in the Netherlands. The model explains the weekly trip rates for each mode in terms of three types of relationships: links from demand for the same mode at previous points in time (temporal stability or inertia); links to and from demand for other modes at the same point in time (complementarity and competition on a synchronous basis); and links from demand for other modes at previous points in time (substitution effects). a significant model is found with 15 inertial links, 21 synchronous links, and 16 cross-lag links among the variables. It is proposed in interpretations of the link coefficients and overall effects of one variable on another that relationships among the modes are evolving over time. In particular, the model captures the effect of a public transit fare increase that occurred during the time frame of the panel data.Dynamic analysis of trip generationMeurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1404742015-05-19T00:02:20Z2015-05-04T21:02:34ZDynamic analysis of trip generation
Article / Letter to editor
Meurs, H.J.
A number of models are presented and estimated describing the correlation of trip making over time. Unobserved heterogeneity is taken into account using random effects. The basic models considered are the serial correlation and the state-dependence model. Trip making in total and by transit was best described using state-dependence models; trip making by car by a model with lagged exogenous variables. The generalized methods of moments procedure is used for estimation of the models: it is asymptotically efficient and does not require assumptions about the initial conditions.
2015-05-04T21:02:34ZA number of models are presented and estimated describing the correlation of trip making over time. Unobserved heterogeneity is taken into account using random effects. The basic models considered are the serial correlation and the state-dependence model. Trip making in total and by transit was best described using state-dependence models; trip making by car by a model with lagged exogenous variables. The generalized methods of moments procedure is used for estimation of the models: it is asymptotically efficient and does not require assumptions about the initial conditions.The Dutch mobility panel: Experiences and evaluationWissen, L.J.G. vanMeurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1404762015-05-19T00:02:21Z2015-05-04T21:02:34ZThe Dutch mobility panel: Experiences and evaluation
Article / Letter to editor
Wissen, L.J.G. van; Meurs, H.J.
The aim of this paper is to give an overview of the history and research experiences of the Dutch National Mobility Panel. Attention is given to the sampling strategy, the policy goals, and the representativity of the panel. It also tries to evaluate the research outcomes in terms of the original objectives and in view of more general research and policy goals. In sections one and two, a historic overview is given, starting from the first ideas to implement a longitudinal research instrument in transportation planning. In section three, some attention is devoted to longitudinal versus cross-sectional analyses. In section four, the sample design is treated in some detail. Next, various forms of bias are discussed that affect the representativity of the panel. In the sixth section, an overview is given of the research conducted with the data. Some conclusions are given in the final section.
2015-05-04T21:02:34ZThe aim of this paper is to give an overview of the history and research experiences of the Dutch National Mobility Panel. Attention is given to the sampling strategy, the policy goals, and the representativity of the panel. It also tries to evaluate the research outcomes in terms of the original objectives and in view of more general research and policy goals. In sections one and two, a historic overview is given, starting from the first ideas to implement a longitudinal research instrument in transportation planning. In section three, some attention is devoted to longitudinal versus cross-sectional analyses. In section four, the sample design is treated in some detail. Next, various forms of bias are discussed that affect the representativity of the panel. In the sixth section, an overview is given of the research conducted with the data. Some conclusions are given in the final section.A panel data switching regression model of mobility and car ownershipMeurs, H.J.http://repository.ubn.ru.nl:80/handle/2066/1404732015-05-20T00:02:03Z2015-05-04T21:02:34ZA panel data switching regression model of mobility and car ownership
Article / Letter to editor
Meurs, H.J.
The objective of this paper is to present a panel data model of car ownership and mobility. Unobserved heterogeneity is controlled for by including correlated random effects in the equations describing car ownership and mobility. A mass-points approach is adopted to control for unobserved heterogeneity. The results show that decisions concerning the first car in the household are difficult to affect; a large number of households are inclined to keep one car. Second car ownership may be more sensitive to changes in the observed contributing factors. This suggests that in The Netherlands policies aimed at changing second car ownership will be more successful than those aimed at influencing decisions concerning the first car in households. A major part of the correlation between the unobservables in the car ownership and the mobility equations is attributable to random effects. The time-variant errors of the mobility equations are not significantly correlated to car ownership decisions. This implies that mobility can only be influenced to a small extent by policy makers without measures aimed at reducing (second) car ownership.
2015-05-04T21:02:34ZThe objective of this paper is to present a panel data model of car ownership and mobility. Unobserved heterogeneity is controlled for by including correlated random effects in the equations describing car ownership and mobility. A mass-points approach is adopted to control for unobserved heterogeneity. The results show that decisions concerning the first car in the household are difficult to affect; a large number of households are inclined to keep one car. Second car ownership may be more sensitive to changes in the observed contributing factors. This suggests that in The Netherlands policies aimed at changing second car ownership will be more successful than those aimed at influencing decisions concerning the first car in households. A major part of the correlation between the unobservables in the car ownership and the mobility equations is attributable to random effects. The time-variant errors of the mobility equations are not significantly correlated to car ownership decisions. This implies that mobility can only be influenced to a small extent by policy makers without measures aimed at reducing (second) car ownership.Measurement biases in panel dataMeurs, H.J.Wissen, L.J.G. vanVisser, Jhttp://repository.ubn.ru.nl:80/handle/2066/1404752015-05-20T00:03:08Z2015-05-04T21:02:34ZMeasurement biases in panel data
Article / Letter to editor
Meurs, H.J.; Wissen, L.J.G. van; Visser, J
The objective of this paper is to examine reporting errors in panel data obtained from multi-day travel diaries. A distinction is made between within and between wave biases. The former leads to an increase in under-reporting associated with the number of days the diary is kept. The latter is related to the number of waves respondents have been participating, so-called panel experience. These biases imply that observed mobility changes between waves are partly due to reporting errors: without controlling for them, changes in mobility can not be inferred from the data. An important cause of these measurement errors is the increase in the number of days on which no trips at all were reported. In addition, shorter trips and less complex chains are more susceptible to underreporting. The methodology used in this paper provides a means of dealing with these problems. Attrition is taken into account by a rather simple measure. The paper concludes with a number of suggestions for sample and survey design.
2015-05-04T21:02:34ZThe objective of this paper is to examine reporting errors in panel data obtained from multi-day travel diaries. A distinction is made between within and between wave biases. The former leads to an increase in under-reporting associated with the number of days the diary is kept. The latter is related to the number of waves respondents have been participating, so-called panel experience. These biases imply that observed mobility changes between waves are partly due to reporting errors: without controlling for them, changes in mobility can not be inferred from the data. An important cause of these measurement errors is the increase in the number of days on which no trips at all were reported. In addition, shorter trips and less complex chains are more susceptible to underreporting. The methodology used in this paper provides a means of dealing with these problems. Attrition is taken into account by a rather simple measure. The paper concludes with a number of suggestions for sample and survey design.