Transportation Research Record

The potential to moderate travel demand through changes in the built environment is the subject of more than 50 recent empirical studies. The majority of recent studies are summarized. Elasticities of travel demand with respect to density, diversity, design, and regional accessibility are then derived from selected studies. These elasticity values may be useful in travel forecasting and sketch planning and have already been incorporated into one sketch planning tool, the Environmental Protection Agency’s Smart Growth Index model. In weighing the evidence, what can be said, with a degree of certainty, about the effects of built environments on key transportation “outcome” variables: trip frequency, trip length, mode choice, and composite measures of travel demand, vehicle miles traveled (VMT) and vehicle hours traveled (VHT)? Trip frequencies have attracted considerable academic interest of late. They appear to be primarily a function of socioeconomic characteristics of travelers and secondarily a function of the built environment. Trip lengths have received relatively little attention, which may account for the various degrees of importance attributed to the built environment in recent studies. Trip lengths are primarily a function of the built environment and secondarily a function of socioeconomic characteristics. Mode choices have received the most intensive study over the decades. Mode choices depend on both the built environment and socioeconomics (although they probably depend more on the latter). Studies of overall VMT or VHT find the built environment to be much more significant, a product of the differential trip lengths that factor into calculations of VMT and VHT.

Safety is emerging as an area of increased attention and awareness within transportation engineering. Historically, the safety of new and innovative traffic treatments has been difficult to assess, primarily because of a lack of good predictive models of crash potential and a lack of consensus on what constitutes a safe or unsafe facility. An FHWA-sponsored research project investigated the potential to derive surrogate measures of safety from existing traffic simulation models. These surrogate measures could then be used to support evaluations of various traffic engineering alternatives, including facilities that have not yet been built and strategies that have not yet been used. Each surrogate measure is collected on the basis of the occurrence of a conflict event, which is an interaction between two vehicles in which one vehicle must take evasive action to avoid a collision. The surrogate measures that are proposed as the best are time to collision, postencroachment time, deceleration rate, maximum speed, and speed differential. Time to collision, postencroachment time, and deceleration rate can be used to measure the severity of the conflict. Maximum speed and the speed differential can be used to measure the severity of the potential collision (by use of additional information about the mass of the vehicles involved to assess momentum). After the simulation model is executed for a number of iterations, a postprocessing tool would be used to compute the statistics for the various measures and perform comparisons between design alternatives.

Across the United States, urban sprawl, its impacts, and appropriate containment policies have become the most hotly debated issues in urban planning. Today’s debates have no anchoring definition of sprawl, which has contributed to their unfocused, dogmatic quality. Efforts to measure sprawl and test for relationships between sprawl and transportation outcomes are described. This is the first use of the newly minted Rutgers–Cornell sprawl indicators. Sprawl is operationalized by combining many variables into a few factors representing density, land use mix, degree of centering, and street accessibility. This consolidation of variables is accomplished with principal component analysis. These factors are then related to vehicle ownership, commute mode choice, commute time, vehicle miles traveled per capita, traffic delay per capita, traffic fatalities per capita, and 8-h ozone level. These associations are made with multiple regression analysis. For most travel and transportation outcomes, sprawling regions perform less well than compact ones. The exceptions are average commute time and annual traffic delay per capita, which do not clearly favor compactness over sprawl. The main limitation of this study has to do with the data it uses. By necessity, the study uses highly aggregate data from a variety of sources that are not always consistent as to the area under study and time period. They are simply the best data available from national sources with sufficient breadth to provide a panoramic view of sprawl in the United States. Results will have to be validated through follow-up work of a more focused nature.

The Next Generation Simulation (NGSIM) trajectory data sets provide longitudinal and lateral positional information for all vehicles in certain spatiotemporal regions. Velocity and acceleration information cannot be extracted directly because the noise in the NGSIM positional information is greatly increased by the necessary numerical differentiations. A smoothing algorithm is proposed for positions, velocities, and accelerations that can also be applied near the boundaries. The smoothing time interval is estimated on the basis of velocity time series and the variance of the processed acceleration time series. The velocity information obtained in this way is then applied to calculate the density function of the two-dimensional distribution of velocity and inverse distance and the density of the distribution corresponding to the “microscopic” fundamental diagram. It is also used to calculate the distributions of time gaps and times to collision, conditioned to several ranges of velocities and velocity differences. By simulating virtual stationary detectors, it is shown that the probability for critical values of the times to collision is greatly underestimated when estimated from single-vehicle data of stationary detectors. Finally, the lane-changing process is investigated, and a quantitative criterion is formulated for the duration of lane changes that is based on the trajectory density in normalized coordinates. There is a noisy but significant velocity advantage in favor of the targeted lane that decreases immediately before the change due to anticipatory accelerations.

Growing concerns about environmental issues have led to the consideration of alternatives to current mobility. Electric mobility is one such alternative that is receiving a great deal of attention in Europe. In particular, a new legal framework for the introduction of an electric mobility system in Portugal has recently been set up by the government. A key issue in this system is recharging the batteries and, consequently, the location of charging stations. This paper presents a study on the location of electric-vehicle charging stations for an area of Lisbon, the capital city of Portugal, characterized with a strong concentration of population and employment. This type of area is appropriate for slow charging because vehicles stay parked for several hours within a 24-h period. The methodology used here is based on a maximal covering model to optimize the demand covered within an acceptable level of service and to define the number and capacity of the stations to be installed. The results clearly indicate that this methodology can be useful in the future planning of electric mobility systems.

Growing concern over traffic safety has led to research into prediction of freeway crashes in an advanced traffic management and information systems environment. A crash likelihood prediction model was developed by using real-time traffic flow variables (measured through a series of underground sensors) potentially associated with crash occurrence. The issues related to real-time application, including range of stations and time slice duration to be examined, were also addressed. The methodology used, matched case-control logistic regression, was adopted from epidemiological studies in which every crash is a case and corresponding noncrashes act as controls. The 5-min average occupancy observed at the upstream station during the 5 to 10 min before the crash, along with the 5-min coefficient of variation in speed at the downstream station during the same time, was found to affect crash occurrence most significantly and hence was used to calculate the corresponding log-odds ratio. A threshold value for this ratio may then be set to determine whether the location must be flagged as a potential crash location. It was shown that by using 1.0 as the threshold for the log-odds ratio, more than 69% crash identification was achieved.

The lane-changing model is an important component within microscopic traffic simulation tools. Following the emergence of these tools in recent years, interest in the development of more reliable lane-changing models has increased. Lane-changing behavior is also important in several other applications such as capacity analysis and safety studies. Lane-changing behavior is usually modeled in two steps: ( a) the decision to consider a lane change, and ( b) the decision to execute the lane change. In most models, lane changes are classified as either mandatory (MLC) or discretionary (DLC). MLC are performed when the driver must leave the current lane. DLC are performed to improve driving conditions. Gap acceptance models are used to model the execution of lane changes. The classification of lane changes as either mandatory or discretionary prohibits capturing trade-offs between these considerations. The result is a rigid behavioral structure that does not permit, for example, overtaking when mandatory considerations are active. Using these models within a microsimulator may result in unrealistic traffic flow characteristics. In addition, little empirical work has been done to rigorously estimate the parameters of lane-changing models. An integrated lane-changing model, which allows drivers to jointly consider mandatory and discretionary considerations, is presented. Parameters of the model are estimated with detailed vehicle trajectory data.

The likelihood of a crash or crash potential is significantly affected by the short-term turbulence of traffic flow. For this reason, crash potential must be estimated on a real-time basis by monitoring the current traffic condition. In this regard, a probabilistic real-time crash prediction model relating crash potential to various traffic flow characteristics that lead to crash occurrence, or “crash precursors,” was developed. In the development of the previous model, however, several assumptions were made that had not been clearly verified from either theoretical or empirical perspectives. Therefore, the objectives of the present study were to ( a) suggest the rational methods by which the crash precursors included in the model can be determined on the basis of experimental results and ( b) test the performance of the modified crash prediction model. The study found that crash precursors can be determined in an objective manner, eliminating a characteristic of the previous model, in which the model results were dependent on analysts’ subjective categorization of crash precursors.

Traffic information from probe vehicles has great potential for improving the estimation accuracy of traffic situations, especially where no traffic detector is installed. A method for dealing with probe data along with conventional detector data to estimate traffic states is proposed. The probe data were integrated into the observation equation of the Kalman filter, in which state equations are represented by a macroscopic traffic-flow model. Estimated states were updated with information from both stationary detectors and probe vehicles. The method was tested under several traffic conditions by using hypothetical data, giving considerably improved estimation results compared to those estimated without probe data. Finally, the application of the proposed method was extended to the estimation and short-term prediction of travel time. Travel times were obtained indirectly through the conversion of speeds estimated or predicted by the proposed method. Experimental results show that the performance of travel-time estimation or prediction is comparable to that of some existing methods.

An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range predictions. Tree-based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the manner in which travel times change from day to day was examined, and several metrics to quantify these changes were developed. The metrics can be used as input for travel-time prediction, but they also should be beneficial for other applications, such as calibrating traffic models and planning models.