Multivariate Poisson-lognormal Model Research Articles

Multivariate Approach to Peak-Period Models for Crash Types and Severities in Variable Speed Systems on Freeways

According to current research, safety performance functions (SPFs) based on traditional annual average daily traffic are less suitable for addressing crash risk in traffic-management strategies that typically operate for short time intervals (e.g., peak period). Variable speed limit (VSL)/variable advisory speed (VAS) is a traffic-management strategy which is deployed to achieve speed harmonization typically during the peak hours of weekdays. However, peak-period SPFs for VSL/VAS-implemented freeways considering different crash types and severity levels are still unexplored. Therefore, in this study the multivariate Poisson lognormal (MVPLN) method was applied to estimate the different crash-type and severity-level models. The results showed that by addressing correlations among different crash types and severity levels, MVPLN models outperformed individual univariate Poisson lognormal crash-type and -severity models. From the developed models, several traffic- and roadway-related attributes—such as volume, average speed, standard deviation of speed, segment type (merge, diverge, and weaving), higher number of lanes, and so forth—could be identified as the potential crash-risk factors. Also, the proposed models were successful in capturing the spatial effects of segments (differences between segments downstream and upstream of the location of interest) on crash frequency. Further, the implementation of VSL/VAS strategy was found to be effective in reducing rear-end crashes in the crash-type model as well as property-damage-only (PDO) and C (non-incapacitating) crash severity levels in the crash-severity model. Policymakers and practitioners could benefit from this study in incorporating safety aspects into developing VSL/VAS algorithms.

Multivariate Poisson-Lognormal Models for Predicting Peak-Period Crash Frequency of Joint On-Ramp and Merge Segments on Freeways

Because of a growing crash occurrence in conflict areas, the ramp and merge segments on freeways are a concern for transportation researchers and practitioners. Therefore, short-term safety-performance functions (SPFs) have been proposed to predict crash frequency at a.m. and p.m. peak-period aggregation levels using microscopic traffic-detector data. The proposed short-term crash prediction models could achieve more accuracy and flexibility, give a better understanding of how safety evaluations change over time, and enable the taking of appropriate actions. This study contributes to the literature by using the multivariate Poisson-lognormal (MVPLN) method via an integrated nested Laplace approximation (INLA) approach to investigate the dependency and the correlation between two responses (on-ramp- and merge-related crash frequencies). Models are developed for total crashes (KABCO) and fatal and severe injury crashes (KAB), utilizing 70% of a total 239 and 238 for joint on-ramp and merge segments at a.m. and p.m. peaks, respectively, from three states of a freeway (i.e., Florida, Virginia, and Wisconsin). The traffic and specific geometric data (e.g., the number of lanes, ramp configurations, presence of weaving segment, and interchange connector type) for ramp and merge segments were used as independent variables. The significant variables were found to be the exposure parameters and various geometric feature variables for ramp and merge segments. Results of posterior means for the correlation coefficients between the ramp and merge crash frequencies indicate that a significant correlation exists between the two locations.

Expressway Rear-End Conflict Pattern Classification and Modeling

The study of expressway rear-end conflicts is of great significance to analyze driving behaviors and improve traffic safety. However, research on the classification and modeling of conflict patterns is still lacking. This study aimed to explore conflict patterns and their relationship with influencing factors. The conflict data used in this study was extracted from a trajectory data set that collected 3 h of data during a morning peak hour on an expressway in Shanghai, China. An improved k-means algorithm, which can automatically obtain the optimal number of clusters, was used to classify the conflict events into six conflict patterns. The conflict patterns were interpreted from five aspects: risk level, speed of risk-changing, risk-avoidance response, risk-avoidance attitude, and risk-avoidance action. Furthermore, a multivariate Poisson-lognormal (MVPLN) model considering spatial–temporal correlation was applied. The relationship between the independent variables and the number of each conflict pattern within the spatial–temporal unit was obtained. The root mean square error of the MVPLN model was 0.81. Compared with univariate Poisson model, univariate negative binomial model, and univariate Poisson-lognormal model, the MVPLN model improved by 73.8%, 81.3%, and 29.6% in accuracy respectively. The results of this study can classify expressway rear-end conflict patterns and obtain the number of each conflict pattern within spatial–temporal units using available traffic data.

Hazardous human–wildlife encounters, risk attitudes, and the value of shark nets for coastal recreation

AbstractShark incidents are rare and graphic events, and their consequences can influence the behavior of beach users, including bathers, to a great extent. These incidents can be thought of as a fearsome risk that may lead decision makers to overreact or respond with inaction. This paper examines the reaction of recreational beach users, including bathers, to changes in the risk of shark incidents. In addition to valuing recreational visits to Durban Beach, South Africa, we study the reaction of beach visitors to a hypothetical scenario in which protective shark nets, deployed in coastal waters to protect bathers, are to be removed. To examine potential heterogeneity of the treatment effect in a travel cost‐contingent behavior model, we develop a semiparametric multivariate Poisson lognormal (MPLN) model to jointly analyze observed and stated visit counts. Results show that removing protective shark nets at Durban beach would decrease recreational visits by more than 20%. Applying the semiparametric MPLN model we further find that both the value of a recreational visit and the predicted change in visitation rates vary as a function of whether recreationists usually enter the water, whether they have heard of previous shark incidents, and their general risk attitude.

Impact of working from home on travel behavior of rail and car commuters: A case study in the Tokyo metropolitan area

This study empirically analyzes the impact of working from home (WFH) on travel behavior in the Tokyo metropolitan area. We use a large survey sample divided by the usual travel mode for commuting and quantify the impact of WFH on the number of rail, car, and walking and cycling trips made on a weekday. Two types of trip frequency models are examined: (1) a multivariate Poisson-lognormal (MVPLN) regression model that simultaneously explains the number of trips made by multiple modes and (2) a negative binomial regression (NBR) model. Explanatory variables comprise the place of work, the built environment of the place of residence, and individual and household attributes. The estimation results of the MVPLN model show that the error correlation between the number of trips made by a commuting mode and that by other/non-commuting modes is low for both rail and car commuters, which could justify the application of an NBR model. The estimation results of the NBR model show that the average effect of WFH for the full day (compared with working only outside the home) is a reduction of 1.9 rail trips per day for rail commuters and 1.6 car trips per day for car commuters, with car commuters who live in low-density areas tending to reduce car trips to a lesser extent. Meanwhile, few differences are observed in the reduction in rail trips for rail commuters by population density. Rail commuters tend to walk and cycle more if they work from home for the full day.

Effects of road network characteristics on bicycle safety: A multivariate Poisson-lognormal model

• Multivariate Poisson-lognormal model is applied to model the bicycle crash of different types. • Multivariate correlation between counts of bicycle-bicycle and bicycle-vehicle crashes is accounted. • Population socio-economics, land use, road density, railway station, and intersection density affect the risk of bicycle-related crashes. • Network accessibility is positively associated with bicycle-vehicle and bicycle-bicycle crashes. • Network connectivity is positively associated with bicycle-bicycle crash only. Although cycling has benefits to environment and physical health, bicyclists are vulnerable road users. Prior studies have identified the environment, traffic and road user factors that affect the risk of bicycle-related crashes. However, it is rare that difference in their effects on the risk amongst different bicycle crash types is investigated. For instance, there is possible correlation between counts of different crash types. In this study, multivariate Poisson-lognormal regression method is applied to examine the relationship between bicycle crash frequencies and possible explanatory factors, with which the correlation between bicycle-vehicle and bicycle-bicycle crashes is considered, using the crash data from London in 2018 and 2019. In addition, effects of road network characteristics on bicycle crash frequencies are also considered. Results indicate that proposed multivariate Poisson-lognormal model outperforms conventional univariate Poisson-lognormal models, in term of the metrics like deviance information criterion (DIC). In addition, factors including population socio-economics, land use, and road network characteristics that affect bicycle crash risk are identified. For instance, effects of traffic flow, residential area, network connectivity, and intersection density on the crash counts are different between bicycle-vehicle and bicycle-bicycle crashes. Findings are useful for the implementation of remedial measures that can improve overall bicycle safety in the long run.

Applying a Bayesian multivariate spatio-temporal interaction model based approach to rank sites with promise using severity-weighted decision parameters

Ranking sites with promise is an essential step for cost-effective engineering improvement on roadway traffic safety. This study proposes a Bayesian multivariate spatio-temporal interaction model based approach for ranking sites. The severity-weighted crash frequency and crash rate are used as the decision parameters. The posterior expected rank and posterior mean of the decision parameters are adopted as the statistical criteria. The proposed approach is applied to rank road segments on Kaiyang Freeway in China, which is conducted via programming in the freeware WinBUGS. The results of Bayesian estimation and assessment indicate that incorporating spatio-temporal correlations and interactions into the crash frequency model significantly improves the overall goodness-of-fit performance and affects the identified crash-contributing factors and the estimated safety effects for each severity level. With respect to the ranking results, significant differences are found between those generated from the proposed approach and those generated from the naïve ranking approach and a Bayesian approach based on the multivariate Poisson-lognormal model. Besides, the ranks under the posterior mean criterion are found generally consistent with those under the posterior expected rank criterion.

The Poisson-Lognormal Model as a Versatile Framework for the Joint Analysis of Species Abundances

Joint Species Distribution Models (JSDM) provide a general multivariate framework to study the joint abundances of all species from a community. JSDM account for both structuring factors (environmental characteristics or gradients, such as habitat type or nutrient availability) and potential interactions between the species (competition, mutualism, parasitism, etc.), which is instrumental in disentangling meaningful ecological interactions from mere statistical associations. Modeling the dependency between the species is challenging because of the count-valued nature of abundance data and most JSDM rely on Gaussian latent layer to encode the dependencies between species in a covariance matrix. The multivariate Poisson-lognormal (PLN) model is one such model, which can be viewed as a multivariate mixed Poisson regression model. Inferring such models raises both statistical and computational issues, many of which were solved in recent contributions using variational techniques and convex optimization tools. The PLN model turns out to be a versatile framework, within which a variety of analyses can be performed, including multivariate sample comparison, clustering of sites or samples, dimension reduction (ordination) for visualization purposes, or inferring interaction networks. This paper presents the general PLN framework and illustrates its use on a series a typical experimental datasets. All the models and methods are implemented in the R package PLNmodels, available from cran.r-project.org.

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.

Statistical analysis of spatial distribution of external corrosion defects in buried pipelines using a multivariate Poisson-lognormal model

It is well recognised that the severity of pipeline external corrosion is highly related to the corrosivity of the surrounding soil environment. However, in practice, the explicit effects of the soil corrosivity variables on the spatial distribution of external corrosion defects are largely unknown. This paper presents a novel modelling and predicting approach for pipeline external corrosion defect count data in terms of spatial patterns using on a multivariate Poisson-lognormal (MVPLN) model. The MVPLN model can account for the over-dispersion and unobserved heterogeneity of the defect count data, as well as consider the stochastic correlation between corrosion defects with different spatial patterns. The developed model is applied to a pipeline inspection dataset consisting in-line inspection (ILI) data and corresponding soil corrosivity measurements. Its performance is validated by using cross-validation. A comparison study shows that the MVPLN model provides superior modelling results for the spatial distribution of external corrosion defects over the commonly used univariate count data models. In addition, the obtained model coefficients of the soil corrosivity measurements are discussed, and their estimated impacts on the spatial patterns of corrosion defects are verified qualitatively. The potential application to assess the corrosion severity of non-piggable pipeline segments is further demonstrated.

Public acceptance of and heterogeneity in behavioral beach trip responses to offshore wind farm development in Catalonia (Spain)

A combined travel cost – contingent behaviour survey of residents and tourists in Catalonia is conducted on-site to examine the effects on beach recreational demand of developing an offshore wind farm (OWF) project. The survey considers four potential OWF scenarios with different degrees of visual impact. We allow for heterogeneity in trip preferences among individuals and control for on-site sampling through the use of a random parameters negative binomial (RPNB) model and a Multivariate Poisson log-normal (MPLN) model, respectively. The welfare measures derived from the RPNB model relate to the current beach users only, whereas those from the MPLN model refer to the general population of residents and tourists in Catalonia. The results show the importance of the specific place of location of the OWF project and how the installation of wind turbines would significantly decrease the demand for trips, depending on their degree of visual impacts, leading to a substantial welfare loss. However, the results also show that the project mainly would cause a displacement of trips to other beaches within Catalonia rather than outside Catalonia and that the welfare per trip measures generated by the RPNB and MPLN models substantially differ. Policy implications of these findings are discussed.

Spatiotemporal Modeling and Real-Time Prediction of Origin-Destination Traffic Demand

Traffic demand prediction has been a crucial problem for the planning, scheduling, and optimization in transportation management. The prediction of traffic demand counts for origin-destination (OD) pairs has been considered challenging due to the high variability and complicated spatiotemporal correlations in the data. Though several articles have considered estimating traffic flows from counts observed at specific locations, existing traffic prediction models seldom dealt with spatiotemporal demand count data of certain OD pairs, or they failed to effectively consider domain knowledge of the traffic network to enhance the prediction accuracy of traffic demand. To tackle the aforementioned challenges, we formulate and propose a multivariate Poisson log-normal model with specific parameterization tailored to the traffic demand problem, which captures the spatiotemporal correlations of the traffic demand across different routes and epochs, and automatically clusters the routes based on the demand correlations. The model is further estimated using an expectation-maximization algorithm and applied for predicting future demand counts at the subsequent epochs. The estimation and prediction procedures incorporate Markov chain Monte Carlo sampling to overcome the computational challenges. Simulations as well as a real application on a New York yellow taxi data are performed to demonstrate the applicability and effectiveness of the proposed method. Supplementary materials for this article are available online.

A family of parsimonious mixtures of multivariate Poisson‐lognormal distributions for clustering multivariate count data

Multivariate count data are commonly encountered through high‐throughput sequencing technologies in bioinformatics, text mining, or sports analytics. Although the Poisson distribution seems a natural fit to these count data, its multivariate extension is computationally expensive. In most cases, mutual independence among the variables is assumed; however, this fails to take into account the correlation among the variables usually observed in the data. Recently, mixtures of multivariate Poisson‐lognormal (MPLN) models have been used to analyze such multivariate count measurements with a dependence structure. In the MPLN model, each count is modeled using an independent Poisson distribution conditional on a latent multivariate Gaussian variable. Owing to this hierarchical structure, the MPLN model can account for over‐dispersion as opposed to the traditional Poisson distribution and allows for correlation between the variables. Rather than relying on a Monte Carlo‐based estimation framework, which is computationally inefficient, a fast variational expectation–maximization (EM)‐based framework is used here for parameter estimation. Further, a family of parsimonious mixtures of Poisson‐lognormal distributions is proposed by decomposing the covariance matrix and imposing constraints on these decompositions. Utility of such models is shown using simulated and benchmark datasets.

Does Automated Enforcement Presence Impact Collisions and Crime?

Enforcement agencies generally operate under a strict budget and with limited resources. For this reason, they are continually searching for new approaches to maximize the efficiency and effectiveness of their deployment. The Data-Driven Approaches to Crime and Traffic Safety approach attempts to identify opportunities where increased visibility of traffic enforcement can lead to a reduction in collision frequencies as well as criminal incidents. Previous research developed functions to model collisions and crime separately, despite evidence suggesting that the two events could be correlated. Additionally, there is little knowledge of the implications of automated enforcement programs on crime. This study developed a Multivariate Poisson-Lognormal model for the city of Edmonton to quantify the correlation between collisions and crime and to determine whether automated enforcement programs can also reduce crime within a neighborhood. The results of this study found a high correlation between collisions and crime of 0.72 which indicates that collision hotspots were also likely to be crime hotspots. The results of this paper also showed that increased enforcement presence resulted in reductions not only in collisions but also in crime. If a single deployment can achieve multiple objectives (e.g., reducing crime and collisions), then optimizing an agency’s deployment strategy would decrease the demand on their resources and allow them to achieve more with less.

X vs. Y: an analysis of intergenerational differences in transport mode use among young adults

Recent research has contrasted the travel patterns of young adults of Generation Y (or, synonymously, the Millennial Generation) with the travel patterns of earlier generations of young adults such as those belonging to Generation X. Young adults of Generation Y are found to drive less and in some contexts are found to exhibit more multimodal travel patterns and to use public transit more often. Potential causes for these observed shifts in transport mode use have also been theorised: One view is that period effects in the form of contemporaneous changes in socio-cultural, socio-economic and socio-technical factors are responsible for the observed shifts in transport mode use; another view is that members of Generation Y have inherently different preferences and values due to formative socio-cultural, socio-economic and historical experiences. Motivated by this yet-to-be-resolved dialectic, this paper uses a hierarchical Bayesian multivariate Poisson log-normal model to examine intergenerational differences in transport mode use among young adults. The model is applied to 23 waves of the German Mobility Panel and captures between-cohort and between-period variation of parameters of interest. The trained model informs a counterfactual prediction exercise aiming to decompose intergenerational differences in transport mode use into demography-, cohort-, and period-specific effects. Our findings suggest that all three sets of effects account for intergenerational differences in transport mode use, while the absolute and relative importance of each set of effects vary across transport modes. For the period from 1998 to 2016, two thirds of the decline in car use can be ascribed to period effects; nearly all of the increase in public transit use and 42% of the increase in bicycling can be ascribed to cohort effects.

The influence of zonal configurations on macro-level crash modeling

ABSTRACTThis study investigated the impacts of zonal configurations on macro-level traffic safety analysis for crashes of different severity levels. Bayesian multivariate Poisson-lognormal models with multivariate conditional auto-regressive priors were developed to account for the spatial autocorrelation between adjacent geographical units and correlations among crash types of four ordinal severity levels, i.e. fatality, severe injury, slight injury and no injury. For the purpose of evaluating the effects of zonal configurations on macro-level traffic safety analysis, the proposed model was calibrated using crash data of four types of geographical units, i.e. block group, traffic analysis zone, census tract and zip code tabulation area, in Hillsborough County of Florida. The study empirically revealed the extensive presence and the significance of MAUP in macro-level safety analysis based on the existing zonal configurations. It gave out a warning and encouraged more research efforts on rational application of macroscopic safety analysis with different zonal configurations.

Multivariate Poisson Lognormal Modeling of Weather-Related Crashes on Freeways

Adverse weather conditions are one of the primary causes of motor vehicle crashes. To identify the factors contributing to crashes during adverse weather conditions and recommend cost-effective countermeasures, it is necessary to develop reliable crash prediction models to estimate weather-related crash frequencies. To account for the variations in crash count among different adverse weather conditions, crash types, and crash severities for both rain- and snow-related crashes, crash data on freeways was collected from the State of Connecticut, and crash prediction models were developed to estimate crash counts by crash type and severity for each weather condition. To account for the potential correlations among crash type and severity counts due to the common unobserved factors, integrated nested Laplace approximation (INLA) multivariate Poisson lognormal (MVPLN) models were developed to estimate weather-related crashes counts by crash type and severity simultaneously (four MVPLN models were estimated in total). To verify the model prediction ability, univariate Poisson lognormal (UPLN) models were estimated and compared with the MVPLN models. The results show that the effects of factors contributing to crashes, including median width, horizontal curve, lane width, and shoulder width, vary not only among different adverse weather conditions, but also among different crash types and severities. The crash types and severities are shown to be highly correlated and the model comparison verifies that the MVPLN models significantly improve the model prediction accuracy compared with the UPLN models. Therefore, the MVPLN model is recommended to provide more unbiased parameter estimates when estimating weather-related crashes by crash type and severity.

Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis

According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li etal., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian etal., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.

X vs. Y: An Analysis of Inter-Generational Differences in Transport Mode Use Among Young Adults

Recent research has contrasted the travel patterns of young adults of Generation Y (or, synonymously, the Millennial Generation) with the travel patterns of earlier generations of young adults such as those belonging to Generation X. Young adults of Generation Y are found to drive less and, in some contexts, are found to exhibit more multimodal travel patterns and to use public transit more often. Potential causes for these observed shifts in transport mode use have also been theorised: One view is that period effects in the form of contemporaneous changes in socio-cultural, socio-economic and socio-technical factors are responsible for the observed shifts in transport mode use; another view is that members of Generation Y have inherently different preferences and values due to formative socio-cultural, socio-economic and historical experiences. Motivated by this yet-to-be-resolved dialectic, this paper uses a hierarchical Bayesian multivariate Poisson log-normal model to examine intergenerational differences in transport mode use among young adults. The model is applied to 23 waves of the German Mobility Panel and captures between-cohort and between-period variation of parameters of interest. The trained model informs a counterfactual prediction exercise aiming to decompose intergenerational differences in transport mode use into demography-, cohort-, and period-specific effects. Our findings suggest that all three sets of effects account for intergenerational differences in transport mode use, while the absolute and relative importance of each set of effects vary across transport modes. For the period from 1998 to 2016, two thirds of the decline in car use can be ascribed to period effects; nearly all of the increase in public transit use and 42% of the increase in bicycling can be ascribed to cohort effects.