Elke Moons

Assessing the Impact of Weather on Traffic Intensity

Abstract This paper focuses on the effect of weather conditions on daily traffic intensities (the number of cars passing a specific segment of a road). The main objective is to examination whether or not weather conditions uniformly alter daily traffic intensities in Belgium, or in other words whether or not road usage on a particular location determines the size of the impacts of various weather conditions. This general examination is a contribution that allows policymakers to assess the appropriateness of countrywide versus local traffic management strategies. In addition, a secondary goal of this paper is to validate findings in international literature within a Belgian context. To achieve these goals, the paper analyzes the effects of weather conditions on both upstream (toward a specific location) and downstream (away from a specific location) traffic intensities at three traffic count locations typified by a different road usage. Perhaps the most interesting results of this study for policymakers ar...

Changes in Travel Behavior in Response to Weather Conditions: Do Type of Weather and Trip Purpose Matter?

Weather can influence travel demand, traffic flow, and traffic safety. A hypothesis—the type of weather determined the likelihood of a change in travel behavior, and changes in travel behavior because of weather conditions depended on trip purpose—was assayed. A stated adaptation study was conducted in Flanders (the Dutch-speaking region of Belgium). A survey, completed by 586 respondents, was administered both on the Internet and as a traditional paper-and-pencil questionnaire. To ensure optimal correspondence between the survey sample composition and the Flemish population, observations in the sample were weighted. To test the main hypotheses, Pearson chi-square independence tests were performed. Results from both the descriptive analysis and the independence tests confirm that the type of weather matters and that changes in travel behavior in response to these weather conditions are highly dependent on trip purpose. This dependence of behavioral adjustments on trip purpose provides policy makers with a deeper understanding of how weather conditions affect traffic. Further generalizations of the findings are possible by shifting the scope toward revealed travel behavior. Triangulation of both stated and revealed travel behavior on the one hand and traffic intensities on the other hand is a key challenge for further research.

Investigating the Variability in Daily Traffic Counts Through Use of ARIMAX and SARIMAX Models: Assessing the Effect of Holidays on Two Site Locations

In this paper, daily traffic counts are explained and forecast by different modeling philosophies: an approach using autoregressive integrated moving average (ARIMA) models with explanatory variables (i.e., the ARIMAX model) and approaches using a seasonal autoregressive integrated moving average (SARIMA) model as well as a SARIMA model with explanatory variables (i.e., the SARIMAX model). Special emphasis is placed on the investigation of seasonality in daily traffic data and on the identification and comparison of holiday effects at different sites. To get insight into prior cyclic patterns in the daily traffic counts, spectral analysis provides the required framework to highlight periodicities in the data. The analyses use data from single inductive loop detectors, which were collected in 2003, 2004, and 2005. Four traffic count locations are investigated in this study: an upstream and a downstream traffic count site on a highway used extensively by commuters, and an upstream and a downstream traffic count site on a highway typically used for leisure travel. The different modeling techniques show that weekly cycles appear to determine the variation in daily traffic counts. The comparison between seasonal and holiday effects at different site locations reveals that both the ARIMAX and the SARIMAX modeling approaches are valid frameworks for identifying and quantifying possible influencing effects. The techniques yield the insight that holidays have a noticeable impact on highways extensively used by commuters, while having a more ambiguous impact on highways typically used for leisure travel. Future research challenges are the modeling of daily traffic counts on secondary roads and the simultaneous modeling of underlying reasons for travel and revealed traffic patterns.

Investigating Effect of Holidays on Daily Traffic Counts: Time Series Approach

Different modeling philosophies are explored to explain and to forecast daily traffic counts. The main objectives of this study are the analysis of the impact of holidays on daily traffic and the forecasting of future traffic counts. Data from single inductive-loop detectors, collected in 2003, 2004, and 2005, were used for the analysis. The different models investigated showed that the variation in daily traffic counts could be explained by weekly cycles. The Box-Tiao modeling approach was applied to quantify the effect of holidays on daily traffic. The results showed that traffic counts were significantly lower for holiday periods. When the different modeling techniques were compared with a large forecast horizon, Box-Tiao modeling clearly outperformed the other modeling strategies. Simultaneous modeling of both the underlying reasons of travel and the revealed traffic patterns certainly is a challenge for further research.

Identifying hazardous road locations: hot spots versus hot zones

Traffic safety has become top priority for policy makers in most European countries. The first step is to identify hazardous locations. This can be carried out in many different ways, via (Bayesian) statistical models or by incorporating the spatial configuration by means of a local indicator of spatial association. In this paper, the structure of the underlying road network is taken into account by applying Morans I to identify hot spots. One step further than the pure identification of hazardous locations is a deeper investigation of these hot spots in a hot zone analysis. This extended analysis is important both theoretically in enriching the way of conceptualizing and identifying hazardous locations and practically in providing useful information for addressing traffic safety problems. The results are presented on highways in a province in Belgium and in an urban environment. They indicate that incorporating the hot zone methodology in a hot spot analysis reveals a clearer picture of the underlying hazardous road locations and, consequently, this may have an important impact on policy makers.

Assessing the Quality of Origin–Destination Matrices Derived from Activity Travel Surveys: Results from a Monte Carlo Experiment

To support policy makers combating travel-related externalities, quality data are required for the design and management of transportation systems and policies. To this end, much money has been spent on collecting household- and person-based data. The main objective of this paper is to assess the quality of origin–destination (O-D) matrices derived from household activity travel surveys. To this purpose, a Monte Carlo experiment is set up to estimate the precision of O-D matrices given different sampling rates. The Belgian 2001 census data, containing work- and school-related travel information for all 10,296,350 residents, are used for the experiment. For different sampling rates, 2,000 random stratified samples are drawn. For each sample, three O-D matrices are composed: one at the municipality level, one at the district level, and one at the provincial level. The correspondence between the samples and the population is assessed by using the mean absolute percentage error (MAPE) and a censored version of the MAPE (MCAPE). The results show that no accurate O-D matrices can be derived directly from these surveys. Only when half of the population is queried is an acceptable O-D matrix obtained at the provincial level. Therefore, use of additional information to grasp better the behavioral realism underlying destination choices and collection of information about particular O-D pairs by means of vehicle intercept surveys are recommended. In addition, results suggest using the MCAPE next to traditional criteria to examine dissimilarities between different O-D matrices. An important avenue for further research is the investigation of the effect of sampling proportions on travel demand model outcomes.

Assessing the Impact of Public Holidays on Travel Time Expenditure: Differentiation by Trip Motive

The impact of public holidays on the underlying reasons for travel behavior, namely, the activities people perform and the trips made, is seldom investigated. Therefore, the effect of holidays on travel time expenditure in Flanders, differentiated by trip motive, is examined. The data used for the analysis stem from a household travel survey carried out in 2000. The zero-inflated Poisson regression approach is used; it explicitly takes into account the inherent contrast between travelers and nontravelers. The zero-inflated Poisson regression models yield findings that are harmonious with international literature: sociodemographic variables, temporal effects, and transportation preferences contribute significantly to unraveling the variability of travel behavior. In particular, it is shown that the effect of public holidays on daily travel behavior cannot be ignored. Triangulation of quantitative and qualitative techniques is a solid basis for insight into the underpinnings of travel behavior.

Road Pricing as an Impetus for Environment-Friendly Travel Behavior: Results from a Stated Adaptation Experiment

An important policy instrument for governments to modify travel behavior and manage the increasing travel demand is the introduction of a congestion pricing system. In this study, the influence of a detailed classification of activities is examined to assess likely traveler response to congestion pricing scenarios. Despite the fact that most studies do not differentiate between activity categories, the value of time and in general the space–time properties and constraints of different types of activities vary widely. For this reason, it is of importance to provide sufficient detail and sensitivity in assessing the impact of congestion pricing scenarios. In addition, a first assessment of travelers’ possible multifaceted adaptation patterns is presented. For these purposes, a stated adaptation study was conducted in Flanders, the Dutch-speaking region of Belgium. The experiment was conducted through an interactive stated adaptation survey. In the stated adaptation experiment, respondents could indicate their responses to the congestion pricing scenario. The most prevalent conclusion is that the activity type significantly predetermines the willingness to express a more environment-friendly behavior (i.e., reducing the number of trips, reducing the total distance traveled, switching to more environment-friendly modes). Also, the willingness to show ecological activity-travel behavior (e.g., carpooling and using public transport) in a nonpricing situation is a major differentiator of future behavior in a congestion pricing scenario.

Impact of Data Integration on Some Important Travel Behavior Indicators

Travel surveys are one of the most important ways of obtaining the critical information needed for transportation planning and decision making today. Reliable and quality data from household travel surveys also demand large sample sizes. These surveys are notoriously expensive, however, and highly time-consuming, and they are faced with a high response burden and subsequent low response rates. Although methodological and technological survey techniques have become increasingly refined, high unit costs and public resistance will continue to plague future survey efforts. This paper investigates the impact of combining survey data from different sources on some important travel behavior indicators. Given the availability of other types of surveys such as time use surveys, which tend to collect a great deal of important data in regard to peoples travel, a wealth of information can be obtained through combining these data with the available travel survey data. Initially, survey data were weighted on the basis of census data on some important socio-demographic characteristics that had been shown to have an impact on travel. Hereby, the fact that census data had the required sample size but generally did not have the required information on travel was exploited. The Flemish travel survey data were then combined with the Flemish time use survey data. The resultant combined data set offered a larger and more representative sample of the population, which gave more reliable travel information on the population. The larger sample was valuable in the prediction of travel demand and can also be used as a base for simulating travel data.

Improving Moran’s Index to Identify Hot Spots in Traffic Safety

This chapter aims at identifying accident hot spots by means of a local indicator of spatial association (LISA), more in particular Moran’s I. A straightforward use of this LISA is impossible, since it is not tailor-made for applications in traffic safety. First of all, road accidents occur on a network, so Moran’s I needs to be adapted to account for this. Moreover, its regular distributional properties are not valid under the circumstances of Poisson distributed count data, as is the case for accidents. Therefore, a Monte Carlo simulation procedure is set up to determine the correct distribution of the indicator under study, though this can be generalized to any kind of LISA. Moran’s I will be adapted in such a way, that it can overcome all the previously stated problems. Results are presented on highways in a province in Flanders and in a city environment. They indicate that an incorrect use of the underlying distribution would lead to false results. Next to this, the impact of the weight function is thoroughly investigated and compared in both settings. The obtained results may have a large impact for policy makers, as money could be allocated in a completely wrong way when an unadjusted LISA is used.