Pedestrian gap acceptance for mid-block street crossing

Abstract

This paper investigates pedestrians' traffic gap acceptance for mid-block street crossing in urban areas. A field survey was carried out at an uncontrolled mid-block location in Athens, Greece. Pedestrians' decisions and traffic conditions were videotaped in terms of the size of traffic gaps rejected or accepted, waiting times and crossing attempts and vehicle speeds. A lognormal regression model was developed to examine pedestrian gap acceptance. It was found that gap acceptance was better explained by the distance from the incoming vehicle, rather than its speed. Other significant effects included illegal parking, presence of other pedestrians and incoming vehicles’ size. A binary logistic regression model was developed to examine the effect of traffic gaps and other parameters on pedestrians' decisions to cross the street or not. The results reveal that this decision is affected by the distance from the incoming vehicles and the waiting times of pedestrians.     

Design policies for sight distance at stop-controlled intersections based on gap acceptance

The current AASHTO policy for sight distance at stop-controlled intersections is based on a model of the acceleration performance of a minor-road vehicle turning left or right onto a major road and the deceleration performance of the following major-road vehicle. This paper develops and quantifies an alternative intersection sight distance model based on gap acceptance. The paper describes field studies that were performed to determine the critical gaps appropriate for use in sight distance design. It is recommended that the sight distance along the major road for a passenger car at a stop-controlled intersection be based on a distance equal to 7.5 s of travel time at the design speed of the major road. Longer sight distances are recommended for minor-road approaches that have sufficient truck volumes to warrant consideration of a truck as the design vehicle. ©     

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times.     

Statistical estimation of the parameters of Daganzo's gap acceptance model

In a recent article in Transportation Research, Daganzo (1981) described a model of gap acceptance that permits the mean of the gap acceptance function to vary among drivers and permits the duration of the shortest acceptable gap for each driver to vary among gaps. The model contains several constant parameters whose values must be estimated statistically from observations of drivers' behavior. The results of numerical experiments reported by Daganzo (1981) suggested that the values of the parameters cannot be estimated by the method of maximum likelihood, which is the most obvious estimation technique, and Daganzo proposed using a sequential estimation method instead. The sequential method appeared to yield reasonable numerical results. In this paper, it is shown that subject to certain reasonable assumptions concerning the true parameter values and the probability distribution of gap durations, the maximum likelihood method does, in fact, yield consistent estimates of the parameters of Daganzo's model, whereas the sequential method does not. Hence, maximum likelihood is the better estimation method for this model.     

Estimation of gap acceptance parameters within and across the population from direct roadside observation

This paper explores the feasibility of maximum likelihood as an approach to determine the parameters of gap acceptance functions when these functions vary from individual to individual. Specifically, it is shown that it is theoretically possible to estimate the average critical gap of a population of drivers (or pedestrians) and its variance, within and across individuals, from direct roadside observations. Although the Multinomial Probit Model provides a natural theoretical framework for the estimation of these parameters, the model seems not to be statistically estimable for this particular problem. It was shown, however, that if one of the parameters is known, the other two become estimable and a two-stage estimation process that takes into account this phenomenon can be utilized. The technique is demonstrated with the 203-driver data set included in Appendix A. The Multinomial Probit Model can also be used to determine simultaneously the mean critical gap, the mean critical lag (the first gap considered by a driver), and the variances of these. For the data set in Appendix A, the mean critical gap was significantly smaller than the mean critical lag, as one might expect. The techniques proposed in this paper have the further advantage of being statistically efficient with large data sets and of not requiring a panel of individuals to be observed under controlled conditions.     

Using time-series designs to estimate changes in freeway level of service,despite missing data

Until recently, time-series designs have seen little use in evaluating freeway management schemes, but the automatic collection of freeway traffic flow data by permanent detectors now makes such analyses feasible. Since detector failures and other factors may produce data gaps in a series, practical, general models must permit multivariate estimation despite some missing data points. Recent developments in time-series analysis make this possible. Using time-series regression analyses (Harvey and Philips, 1979; Jones 1985), it is possible to detect relatively small average changes in traffic flow characteristics, such as peak hour volume and lane occupancy. These can then be related to the freeway's level of service.     

Using AHP to measure the perception gap between current and potential users of bus services

Abstract

This study develops a multicriteria evaluation of user perception towards bus transit services and measures the gap in the perceptions held by current and potential users. A review of the transport quality literature indicates that both preference and satisfaction measures have been implemented to provide a comprehensive perception-based evaluation of bus quality. Although attempts have been made to evaluate user perception through a separate analysis of each (preference and/or satisfaction), the application of multicriteria quality measures are limited. A multicriteria quality measure not only offers more than just information on the daily experience of users but also knowledge of the internal process of quality evaluation (drivers/barriers). The multicriteria measure utilises the data of 512 questionnaires, whereby user perceptions were expressed through judgments of importance and satisfaction based on a set of 29 quality indicators classified into six attributes. Firstly, the study develops analytical hierarchy process (AHP) models to measure user preference. Secondly, a weighted perception index (WPI) of both preference and satisfaction is developed through a multicriteria model. Finally, multivariate analysis of variance (MANOVA) is conducted to identify the level of variation in the perception of both current and potential users towards bus service quality. The results show that although both current and potential users place a higher importance towards the same indicators, they do not share the same pattern of preferences (values and/or order). In addition, the study found that the interaction between preferences and satisfaction generated new patterns of subjective evaluation, and that these patterns vary significantly by user category.     

Comparing decision tree algorithms to estimate intercity trip distribution

Traditional trip distribution models usually ignore the fact that destination choices are made individually in addition to aggregated factors, such as employment and average travel costs. This paper proposes a disaggregated analysis of destination choices for intercity trips, taking into account aggregated characteristics of the origin city, an impedance measurement and disaggregated variables related to the individual, by applying nonparametric Decision Tree (DT) algorithms. Furthermore, each algorithm’s performance is compared with traditional gravity models estimated from a stepwise procedure (1) and a doubly constrained procedure (2). The analysis was based on a dataset from the 2012 Origin-Destination Survey carried out in Bahia, Brazil. The final selected variables to describe the destination choices were population of the origin city, GDP of the origin city and travel distances at an aggregated level, as well as the variables: age, occupation, level of education, income (monthly), number of cars per household and gender at a disaggregated one. The comparison of the DT models with gravity models demonstrated that the former models provided better accuracy when predicting the destination choices (trip length distribution, goodness-of-fit measures and qualitative perspective). The main conclusion is that Decision Tree algorithms can be applied to distribution modeling to improve traditional trip distribution approaches by assimilating the effect of disaggregated variables.     

An innovative model to estimate the accessibility of a destination by public transport

Accessibility is a valuable indicator for assessing the effectiveness of a transport network. Nevertheless, its analysis can lead to very heterogeneous results depending on the method adopted, thus still struggling to be considered by policy makers and in planning processes. This article contributes to the development of an accessibility analysis as a planning tool, by proposing an alternative model to estimate the relative accessibility of a destination by public transport (PT). Indeed, PT is a mobility paradigm that has been supported to reduce the negative externalities produced by private transport, especially in environmentally fragile contexts. The model includes a set of eleven factors affecting public transport operation which refer to four macro topics: connectivity, multimodality, tariff/ticketing, and info-mobility. They are integrated into a Public Transport Accessibility index which analyses a series of nodes along a route and detects the progressive variation of accessibility. Eventual shifts are highlighted, including information about the factors feeding them. The model is tested for the Lana-Zurich (IT-CH) connection, showing how the most relevant issues are caused by difficulties in the transnational and transregional integration of the services. Through this process, the model aims at backing policy makers in the detection and understanding of public transport barriers and related causes.     

An assessment of models to estimate pedestrian demand based on the level of activity

This paper presents the development and assessment of models to estimate pedestrian demand based on the level of pedestrian activity (high and low). As activity varies by the time of the day, temporal variations were evaluated by considering different time periods. Data collected at 128 low and 48 high pedestrian activity signalized intersections (a total of 176 signalized intersections) in the City of Charlotte, North Carolina were used to develop and assess the models using stepwise regression analysis through backward elimination of independent variables (includes demographic, land use, and network characteristics). The use of different buffer widths (proximal area) to extract these characteristics was also evaluated. Results, in general, show that pedestrian demand varied by the level of activity, explanatory variables extracted by buffer width, and time of the day. The estimates from the models could be used in transportation planning (identify required pedestrian facilities, resource allocation), safety, and operational analyses. Copyright © 2010 John Wiley & Sons, Ltd.     

A methodology to estimate the revenue anticipated from State-level alternative financing approaches in the US

A major issue that State Departments of Transportation (DOTs) in the US face relates to financing future transportation investments. Questions of interest to State DOT officials relate to the suitability and revenue potential of alternative financing approaches. This paper presents a methodology to estimate the potential levels of revenue associated with the use of selected combinations of financing approaches and to assess the adequacy of these revenues vis-a-vis various levels of investments being considered by decision-makers. The methodology is designed to accommodate a wide array of inputs such as major policy objectives and initial assumptions that may vary significantly from State to State in order to provide a greater flexibility of implementation. The application of this methodology is demonstrated with an illustrative example for Massachusetts. This paper should be of interest to State DOTs seeking an acceptable combination of financing approaches to support future transportation investments.     

A practical method to estimate the benefits of improved road network reliability: an application to departing air passengers

This paper develops and applies a practical method to estimate the benefits of improved reliability of road networks. We present a general methodology to estimate the scheduling costs due to travel time variability for car travel. In contrast to existing practical methods, we explicitly consider the effect of travel time variability on departure time choices. We focus on situations when only mean delays are known, which is typically the case when standard transport models are used. We first show how travel time variability can be predicted from mean delays. We then estimate the scheduling costs of travellers, taking into account their optimal departure time choice given the estimated travel time variability. We illustrate the methodology for air passengers traveling by car to Amsterdam Schiphol Airport. We find that on average planned improvements in network reliability only lead to a small reduction in access costs per trip in absolute terms, mainly because most air passengers drive to the airport outside peak hours, when travel time variability tends to be low. However, in relative terms the reduction in access costs due to the improvements in network reliability is substantial. In our case we find that for every 1 Euro reduction in travel time costs, there is an additional cost reduction of 0.7 Euro due to lower travel time variability, and hence lower scheduling costs. Ignoring the benefits from improved reliability may therefore lead to a severe underestimation of the total benefits of infrastructure improvements.     

Integrating multi-source maritime information to estimate ship exhaust emissions under wind,wave and current conditions

How to accurately calculate ship exhaust emissions has become urgent needs. In this paper, multi-source maritime information is integrated to estimate ship exhaust emissions under ocean environment. Influences of wind, wave and current on ship speed are firstly analyzed and mathematically modeled. Based on the influences, ocean environment information and ship trajectories are integrated to identify ship activities exactly. After that, ship activity based calculation method is present to obtain exhaust emissions from ship in various activities. Contribution ratios of different ship type and ship activities have been further discussed. In a case study of Ningbo-Zhoushan port in China, greenhouse gas (CO₂, CO, SO_x, NO_x and PM) emissions from ships in 2014 calculated by the proposed method are 8.72 × 10⁵ ton, 2.07 × 10³ ton, 1.47 × 10⁴ ton, 2.60 × 10⁴ ton and 1.40 × 10³ ton respectively. The maximum error is under 10%. Experimental results illustrate that the proposed method can produce more accurate ship exhaust emissions than traditional method under ocean environment conditions.     

A methodology to estimate travel time using dynamic traffic assignment (DTA) under incident conditions

This paper presents results from a research case study that examined the distribution of travel time of origin–destination (OD) pairs on a transportation network under incident conditions. Using a transportation simulation dynamic traffic assignment (DTA) model, incident on a transportation network is executed under normal conditions, incident conditions without traveler information availability, and incident conditions assuming that users had perfect knowledge of the incident conditions and could select paths to avoid the incident location. The results suggest that incidents have a different impact on different OD pairs. The results confirm that an effective traveler information system has the potential to ease the impacts of incident conditions network wide. Yet it is also important to note that the use of information may detriment some OD pairs while benefiting other OD pairs. The methodology demonstrated in this paper provides insights into the usefulness of embedding a fully calibrated DTA model into the analysis tools of a traffic management and information center.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

Using geometric primitives to calibrate traffic scenes

In this paper, we address the problem of recovering the intrinsic and extrinsic parameters of a camera or a group of cameras in a setting overlooking a traffic scene. Unlike many other settings, conventional camera calibration techniques are not applicable in this case. We present a method that uses certain geometric primitives commonly found in traffic scenes, such as straight and curved lanes, lane markings, and poles in order to recover calibration parameters. We show experimentally that these primitives provide the needed redundancy and are capable of achieving accurate results suitable for most traffic monitoring applications.     

用玻璃纤维织物建造未来桥梁

编者按：尼尔桥是美国建筑师用23条碳和玻璃纤维织物建造出的一座桥梁,如果这种混合设计法能得以流行,那么纤维强化塑料应用于公路将取得重大突破。