Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Although cluster analysis is recommended by the US Traffic Monitoring Guide (TMG) to supplement the development of seasonal adjustment factor groupings (SAFGs), the relationships among SAFGs' characteristics remain undiscovered, while the determination of the optimal number of clusters is an ambiguous task exposed to great subjectivity. Statistical indicators provide a mathematical solution by removing engineering judgment without taking into consideration any guidelines or other criteria, necessary for transportation planners to generate ‘practical and sensible’ groupings. The method examined in this study aims to overcome the above weaknesses incorporating into the methodology a series of statistics, recommendations, and previous research findings. The investigation of the relationships among (1) the within-group variation, (2) the total number of sites, (3) the minimum number of stations within a cluster, (4) the optimal number of clusters, and (5) the geographical size of the groups constitutes the main objectives of this research. According to the results, the cluster variability declines as the available number of stations increases. When the minimum number of stations within a cluster increases, the weighted coefficient of variation inflates as well, with the rate of increase depending on sample size. The average number of automatic traffic recorders per cluster is analogous to the sample size, while the optimal number of clusters varies conversely with the minimum number of stations within a cluster. The application developed for the conduct of the analysis minimizes the computational time needed, while it can be easily implemented by engineers to automate the process recommended by the TMG, enhancing the current state of practice.     

Urban single-lane roundabouts: A new analytical approach using multi-criteria and simultaneous multi-objective optimization of geometry design,efficiency and safety

Building safe and effective roundabouts requires optimizing traffic (operational) efficiency (TE) and traffic safety (TS) while taking into account geometric factors, traffic characteristics and local constraints. Most existing simulation-based optimization models do not simultaneously optimize all these factors. To capture the relationship among geometry, efficiency and safety, we put forward a model formulation in this paper. We present a new multi-criteria and simultaneous multi-objective optimization (MOO) model approach to optimize geometry, TE and TS of urban unsignalized single-lane roundabouts. To the best of our knowledge, this is the first model that uses the multi-criteria decision-making method known as analytic hierarchy process to evaluate and rank traffic parameters and geometric elements of urban single-lane roundabouts. The model was built based on comprehensive review of the research literature and existing roundabout simulation software, a field survey of 61 civil and traffic expert engineers in Croatia, and field studies of roundabouts in the Croatian capital city of Zagreb. We started from the basis of Kimber’s capacity model, HCM2010 serviceability model, and Maycock and Hall's accident prediction model, which we extended by adding sensitivity analysis and powerful MOO procedures of the bounded objective function method and interactive optimization. Preliminary validation of the model was achieved by identifying the optimal and most robust of three geometric alternatives (V.1-V.3) for an unsignalized single-lane roundabout in Zagreb, Croatia. The geometric parameters in variant V.1 had significantly higher values than in the existing design V.0, while approaches 1 and 3 in variant V.2 were enlarged as much as possible within allowed spatial limits and Croatian guidelines, reflecting their higher traffic demand. Sensitivity analysis indicated that variant V.2 showed the overall highest TE and TS across the entire range of traffic flow demand and pedestrian crossing flow demand at approaches. At the same time, the number of predicted traffic accidents was similar for all three variants, although it was lowest overall for V.2. The similarity in predicted accident frequency for the three variants suggests that V.2 provides the greatest safety within the predefined constraints and parameter ranges explored in our study. These preliminary results suggest that the proposed model can optimize geometry, TE and TS of urban single-lane roundabouts.     

Locating rental stations and bikeways in a public bike system

To determine the spatial distribution of rental stations and bikeways in a public bike system, this paper proposes a facility location and network design model. The model is developed as a multi-objective programing problem that considers four objectives (minimizing cyclist risk, maximizing cyclist comfort, minimizing adverse impacts on traffic and maximizing service coverage) and multiple constraints (monetary budget, network connectivity, station spacing, bikeway types, station number and value ranges of decision variables). The ε-constraint method solves the programing problem for the public bike system in Daan District, Taipei City, Taiwan. The nine non-dominated alternatives generated are all markedly better than existing locations of rental stations and bikeways. Scenario analysis results indicate that increasing the construction budget for bikeways significantly improves cyclist safety and comfort whilst increasing the adverse impact on traffic. Planners can use this model to develop public bike systems that spatially integrate rental stations and bikeway networks.     

Passenger travel characteristics and bus operational states: a study based on IC card and GPS data in Yinchuan,China

ABSTRACT

In recent years, public transport has been developing rapidly and producing large amounts of traffic data. Emerging big data-mining techniques enable the application of these data in a variety of ways. This study uses bus intelligent card (IC card) data and global positioning system (GPS) data to estimate passenger boarding and alighting stations. First, an estimation model for boarding stations is introduced to determine passenger boarding stations. Then, the authors propose an innovative uplink and downlink information identification model (UDI) to generate information for estimating alighting stations. Subsequently, the estimation model for the alighting stations is introduced. In addition, a transfer station identification model is also developed to determine transfer stations. These models are applied to Yinchuan, China to analyze passenger flow characteristics and bus operations. The authors obtain passenger flows based on stations (stops), bus lines, and traffic analysis zones (TAZ) during weekdays and weekends. Moreover, average bus operational speeds are obtained. These findings can be used in bus network planning and optimization as well as bus operation scheduling.     

Locating inspection facilities in traffic networks: an artificial intelligence approach

Abstract

In order for traffic authorities to attempt to prevent drink driving, check truck weight limits, driver hours and service regulations, hazardous leaks from trucks, and vehicle equipment safety, we need to find answers to the following questions: (a) What should be the total number of inspection stations in the traffic network? and (b) Where should these facilities be located? This paper develops a model to determine the locations of uncapacitated inspection stations in a traffic network. We analyze two different model formulations: a single-objective optimization problem and a multi-objective optimization problem. The problems are solved by the Bee Colony Optimization (BCO) method. The BCO algorithm belongs to the class of stochastic swarm optimization methods, inspired by the foraging habits of bees in the natural environment. The BCO algorithm is able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are found to be acceptable.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.     

A simulation-based approach in determining permitted left-turn capacities

A fundamental objective of traffic signal operations is the development of phasing plans that reduce delays while maintaining a high level of safety. One issue of concern is the treatment of left-turn phasing, which can operate as a protected movement, a permitted movement yielding to conflicting traffic, a combination protected–permitted movement or as a split-phase intersection. While protected-only movements can improve safety for the turning movement, they can also increase delays and congestion at the intersection. Most states maintain independent guidance for determining left-turn phasing; however, the most common identified guidance for protected left-turn phases is using a threshold based on the cross product of the left-turn volume and opposing through movements. The use of the cross product has been questioned recently as an indicator for determining phase selection. Based on simulation analysis within this research, the cross product is shown to be a poor indicator of left-turn capacity and congestion at the intersection.This research proposes a simplified single variable exponential model to determine left-turn capacity based on opposing volume and percent green time to determine left-turn capacity thresholds for protected left-turn phasing. The model is developed based on observed capacity from 450 VISSIM microsimulation scenarios which evaluated varying opposing volume, opposing number of lanes, cycle lengths and green time splits. Validation of the model based on complex Highway Capacity Manual procedures, indicates that the proposed model provides similar correlation to observed capacities. Finally, a nomograph is developed which presents the model in a simple form for interpretation and application by practicing traffic engineers, when required to determine left-turn phasing options. This procedure allows simple determination based on minimum input data needs similar to the cross product determination, without the need for complex hand calculations or computing requirements of the Highway Capacity Manual.     

基于灰色关联度的交通安全道路影响因素辨析

道路系统中诸因素共同作用决定了系统的交通安全发展态势。如何在众多因素中寻找主要致因,是进行道路安全改善时所必须面对的问题。考虑到数据获取贫信息的特点,采用灰色关联分析,不但可以提高现有数据的利用率,而且弥补常用数理统计方法作系统分析所导致的缺憾。实例分析表明:采用此方法所得到的主要道路影响因素和交通安全之间的关联度大小与实地踏查结果相吻合,结果说明灰色关联度可以简单而高效地进行系统因素辨析。     

A new lost time estimation method for right‐turn traffic in Japan considering signal phasing and sneakers

Precise estimation of the capacity for right‐turn traffic (comparable to left‐turn traffic in the USA) is of great importance to determine signal phasing schemes at signalized intersections in Japan, where the left‐hand driving rule is valid. However, in most signal timing procedures across the world, the lost time of right‐turn traffic is simply determined by the duration of intergreen intervals and thus lacks considerations of various signal phasing and driver behavior. Meanwhile, sneakers per cycle are usually applied to account for the number of drivers completing right turns during the effective red portion of the clearance‐and‐change intervals. As a result, an initial cycle length must be hypothesized in order to assess the total number of sneakers within the analysis period. Consequently, a time‐consuming iterative calculation process often becomes necessary. Therefore, the present study aims to develop a new lost time estimation method for right‐turn traffic to overcome the aforementioned drawbacks. Lost times of right‐turn traffic under three conventional phasing plans are theoretically formulated on the basis of a time–space diagram and shock‐wave theory. The new method is validated using field data, with case studies of its application in the signal timing procedure. Results indicated that the proposed method is capable of offering more accurate estimation than conventional approaches, which leads to shorter cycle length and simplifies signal timing process by eliminating an iterative check to determine the number of sneakers. Copyright © 2012 John Wiley & Sons, Ltd.     

Revisiting the notion of induced traffic through a matched-pairs study

In investigating the question of the existence of "induced demand" in connection with highway expansion projects, Hansen et al. (1993) studied eighteen California state highway segments whose capacities had been improved in the early 1970s. For the present study, these segments were paired with control segments that matched the improved segments to unimproved ones with regard to facility type, region, approximate size, and initial volumes and congestion levels. Taking annual data for average daily traffic (ADT) and design-hour-traffic-to-capacity (DTC) ratios during the 21 years 1976–1996, three approaches were used to compare growth rates between the improved and unimproved segments: overall growth comparisons for the matched pairs, repeated measures analysis, and analysis of matched mean profiles. We found the growth rates between the two types of segments to be statistically and practically indistinguishable, suggesting that the capacity expansions, in and of themselves, had a negligible effect on traffic growth over the period studied. Reasons for the differences between these results and those of aggregate cross-sectional models finding a significant induced demand effect are discussed. Our analyses suggest that the aggregate models may overestimate induced traffic due to the attribution of at least a fraction of the observed traffic growth to "induced demand" rather than to some of the confounding factors which were not controlled for in such studies. At the same time, it is noted that the traffic induced by capacity expansion may in certain circumstances be larger than that observed in the present study, with the effect of new highway construction on traffic growth being a prime candidate for scrutiny in this regard. The results of this study nonetheless suggest that, for existing facilities, the size of the induced-traffic effect that can be attributed to capacity enhancements may be sufficiently small that its detection in a case-control study would be difficult, if not impossible, without a substantially larger sample size.     

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

The precise estimation of the annual average daily traffic (AADT) is a task of significant interest for many transportation authorities and Departments of Transportation. In this study, three methods are developed to improve the assignment of short‐term counts to seasonal adjustment factor (SAF) groupings: the traditional functional classification, a discriminant analysis (DA), and a new statistical approach based on a weighted coefficient of variation (WCV). The data analyzed within this study are generated from all available continuous counters within the State of Ohio between 2002 and 2006. The analysis is conducted using SAFs that are separately calculated for the total volume and the directional specific volumes of a site. The results show that the directionally based assignment errors are statistically lower at a 95% confidence interval when compared with those generated by the total volume analysis. It is also found that the hourly time‐of‐day factors are more important in the assignment process than the average daily traffic. The directionally based WCV produces a decline in the average mean absolute percentage error (MAPE) over the roadway functional classification by 58% and in the standard deviation of the absolute error (SDAE) by 70%. On the contrary, the directionally based DA lowers the MAPE and the SDAE by 35% and 60%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.     

ATCEM: a synthetic model for evaluating air traffic complexity

Air traffic complexity, which measures the disorder of air traffic distribution, has become the critical indicator to reflect air traffic controller workload in air traffic management (ATM) system. However, it is hard to assess the system accurately because there are too many correlated factors, which make the air traffic complexity nonlinear. This paper presents an air traffic complexity evaluation model with integrated classification using computational intelligence (ATCEM). To avoid redundant factors, critical factors contributing to complexity are analyzed and selected from numerous factors in the ATCEM. Subsequently, to construct the mapping relationship between selected factors and air traffic complexity, an integrated classifier is built in ATCEM. With efficient training and learning based on aviation domain knowledge, the integrated classifier can effectively and stably reflect the mapping relationship between selected factors and the category of air traffic complexity to ensure the precision of the evaluation. Empirical studies using real data of the southwest airspace of China show that the ATCEM outperforms a number of state‐of‐the‐art models. Moreover, using the critical complexity factors selected in ATCEM, the air traffic complexity could be effectively estimated. Copyright © 2015 John Wiley & Sons, Ltd.     

Incorporating refuelling behaviour and drivers’ preferences in the design of alternative fuels infrastructure in a city

The purpose of this article is to present an optimization model to plan the deployment strategy for hydrogen refuelling stations in a city when Origin–Destination (OD) data are not available. This model considers two objectives: to maximize the traffic covered by the selected hydrogen refuelling stations and minimize the average distance of the city’s inhabitants to the nearest hydrogen refuelling station. As OD data are assumed to be unavailable, the clustering of stations in the highest traffic zones is prevented by a new constraint that takes into account information on the distribution of existing conventional refuelling stations. This model is applied to Seville, a city in Southern Spain of about 140 km² with a population of around 700,000. This application uses the results of a survey of more than 200 Sevillian drivers on their current refuelling tendencies, their willingness to use alternative fuel vehicles and their minimum requirements (regarding maximum distance to be travelled to refuel and number of stations in the city) when establishing a network of alternative refuelling stations.     

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.     

Extended spectral envelope method for detecting and analyzing traffic oscillations

We propose using a spectral envelope method to analyze traffic oscillations using data collected from multiple sensors. Spectral envelops can reveal not only the salient frequencies of periodic oscillations of traffic flow, but also the relative strength of these oscillations at different locations. This paper first introduces time dimension into the existing spectral envelope method so that it can be applied to study the evolution of vehicular traffic oscillations. The extended spectral envelope method proposed in this paper, or ESPE, discards the normalization procedure in the standard method. A new Contributing Index (CI) is proposed to measure the relative strength of oscillations at different locations. The extended spectral envelops can be constructed on long-term or short-term time scales. While the long-term analysis helps extract salient frequencies of traffic oscillations, the short-term analysis promises to reveal their detailed spatial–temporal profiles. ESPE offers two distinctive advantages. First, it is more robust against the impacts of noises. Second, it is able to uncover complicated oscillatory behaviors which are otherwise difficult to notice. These advantages are demonstrated in case studies constructed on both simulated and real data.     

A method for relating type of crash to traffic flow characteristics on urban freeways

A method is developed to determine how crash characteristics are related to traffic flow conditions at the time of occurrence. Crashes are described in terms of the type and location of the collision, the number of vehicles involved, movements of these vehicles prior to collision, and severity. Traffic flow is characterized by central tendencies and variations of traffic flow and flow/occupancy for three different lanes at the time and place of the crash. The method involves nonlinear canonical correlation applied together with cluster analyses to identify traffic flow regimes with distinctly different crash taxonomies. A case study using data for more than 1000 crashes in Southern California identified twenty-one traffic flow regimes for three different ambient conditions: dry roads during daylight (eight regimes), dry roads at night (six regimes), and wet conditions (seven regimes). Each of these regimes has a unique profile in terms of the type of crashes that are most likely to occur, and a matching of traffic flow parameters and crash characteristics reveals ways in which congestion affects highway safety.     

User perceptions and engineering definitions of highway level of service: an exploratory statistical comparison

The level of service (LOS) concept in the Highway Capacity Manual has been used as a qualitative measure representing freeway operational conditions for over 35 years. One key element that has not been adequately addressed is how road users perceive LOS. This exploratory research examines road-user perceptions of freeway LOS by presenting study participants with a series of video clips of various traffic conditions (taken from cameras on overpasses to allow a complete view of the traffic stream) and asking them their perceptions of LOS. A random effects ordered probability model is then used to statistically link participant-recorded perceptions of LOS with measurable traffic conditions (speed, density, flow, percentage of trucks, vehicle headways) and participant characteristics. The findings suggest that the Highway Capacity Manual’s use of traffic density as a single performance measure for LOS does not accurately reflect road-user perceptions. The statistical analysis shows that a number of attributes besides traffic density determine public perceptions of LOS and that these perceptions vary depending on both traffic conditions and road-user characteristics.     

Recent developments in traffic flow modeling using macroscopic fundamental diagram

ABSTRACT

This paper presents an overview of the recent developments in traffic flow modelling and analysis using macroscopic fundamental diagram (MFD) as well as their applications. In recent literature, various aggregated traffic models have been proposed and studied to analyse traffic flow while enhancing network efficiency. Many of these studies have focused on models based on MFD that describes the relationship between aggregated flow and aggregated density of transport networks. The analysis of MFD has been carried out based on experimental data collected from sensors and GPS, as well as simulation models. Several factors are found to influence the existence and shape of MFD, including traffic demand, network and signal settings, and route choices. As MFD can well express the traffic dynamics of large urban transport networks, it has been extensively applied to traffic studies, including the development of network-wide control strategies, network partitioning, performance evaluation, and road pricing. This work also presents future extensions and research directions for MFD-based traffic modelling and applications.     

Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data

Nowadays, the massive car-hailing data has become a popular source for analyzing traffic operation and road congestion status, which unfortunately has seldom been extended to capture detailed on-road traffic emissions. This study aims to investigate the relationship between road traffic emissions and the related built environment factors, as well as land uses. The Computer Program to Calculate Emissions from Road Transport (COPERT) model from European Environment Agency (EEA) was introduced to estimate the 24-h NOx emission pattern of road segments with the parameters extracted from Didi massive trajectory data. Then, the temporal Fuzzy C-Means (FCM) Clustering was used to classify road segments based on the 24-h emission rates, while Geographical Detector and MORAN’s I were introduced to verify the impact of built environment on line source emissions and the similarity of emissions generated from the nearby road segments. As a result, the spatial autoregressive moving average (SARMA) regression model was incorporated to assess the impact of selected built environment factors on the road segment emission rate based on the probabilistic results from FCM. It was found that short road length, being close to city center, high density of bus stations, more ramps nearby and high proportion of residential or commercial land would substantially increase the emission rate. Finally, the 24-h atmospheric NO₂ concentrations were obtained from the environmental monitor stations, to calculate the time variational trend by comparing with the line source traffic emissions, which to some extent explains the contribution of on-road traffic to the overall atmospheric pollution. Result of this study could guide urban planning, so as to avoid transportation related built environment attributes which may contribute to serious atmospheric environment pollutions.     

Safety modeling for two‐way left‐turn lanes and a procedure to identify two‐way left‐turn lane segments with safety concerns

In the past, two‐way left‐turn lane (TWLTL) median treatments have been frequently used in Florida to inexpensively improve traffic and safety performances. In order to identify factors that may have significant impacts on safety operations in TWLTL sections and to identify TWLTL locations that present existing and future safety concerns, a research project was carried out and results are summarized in the paper. In the research, a three‐year crash history database with crashes and section characteristics from a total of 1688 TWLTL sections all over Florida was developed and used. A negative binomial regression model was developed to determine the statistical relationship between the number of crashes per mile per year and several variables such as traffic volume, access density, posted speed, and number of lanes. In regard to the methodology, in order to identify locations with safety concerns, several steps are needed: development of real crash data distribution, determination of statistical distribution models that better represent the actual crash data, determination of percentile values for the average number of crashes, estimation of crash rates for sections with the same characteristics, estimation of critical values for the variables corresponding to the percentile values for average number of crashes, calculation of tables of critical average annual daily traffic values, and generation of a list of TWLTL locations with critical safety concerns. Results presented in the paper have been used in real applications. Copyright © 2012 John Wiley & Sons, Ltd.