The importance of being early

This research quantifies the relationship between the cost of earliness and lateness by empirically observing commute trips from two different sources. The first empirical analysis uses individual level travel survey data from six metropolitan regions while the second analysis uses traffic data from the Twin Cities freeway network. The analysis conducted in this research provides a method to estimate the ratio of the costs of earliness to lateness for different datasets. This can be a useful tool for traffic engineers and planners, to assist them in the development and implementation of improved control strategies for congested cities. The results also corroborates the hypothesis of earliness being less expensive than lateness and show that the finding holds steady over time and across different regions and levels.     

Simulation‐based estimates of delays at freeway work zones

Work zone related traffic delay is an important cost component on freeways with maintenance activities. This study demonstrates that delays may be underestimated by using the deterministic queuing theory. Computer simulation is a valuable approach of estimating delay under a variety of existing and future conditions. However, a single simulation run, which can be quite costly in terms of both computer and analyst time, produces a delay estimate for only one traffic level under one set of conditions. A method is developed in this paper to approximate delays by integrating limited simulation data, obtained from CORSIM and the concept of deterministic queuing theory, while various geometric conditions and time‐varying traffic distribution are considered. A calibrated and validated simulation model that can reflect work zone traffic operations on a segment of Interstate 1–80 in New Jersey is used to generate data for developing the proposed model. The comparison of delays estimated by the deterministic queuing model and the proposed model is conducted, while factors affecting the accuracy of the delay estimates are discussed.     

System optimum and pricing for the day-long commute with distributed demand,autos and transit

The day-long system optimum (SO) commute for an urban area served by auto and transit is modeled as an auto bottleneck with a capacitated transit bypass. A public agency manages the system’s capacities optimally. Commuters are identical except for the times at which they wish to complete their morning trips and start their evening trips, which are given by an arbitrary joint distribution. They value unpunctuality – their lateness or earliness relative to their wish times – with a common penalty function. They must use the same mode for both trips. Commuters are assigned personalized mode and travel start times that collectively minimize society’s generalized cost for the whole day. This includes unpunctuality penalties, queuing delays, travel times and out-of-pocket costs for users, as well as travel supply costs and externalities for society.It is shown that in a SO solution there can be no queuing and that the set of SO solutions forms a convex set. Furthermore, if the schedule penalty that users suffer due to unpunctuality is separable into morning and evening components, then the set of commuters traveling by the same mode arrive at work and depart from work in the order of their wishes. These orders are in general different in the morning and the evening. It is also shown that there always is a SO solution in which users are at all times, and on both modes, either punctual or flowing at capacity. These problem properties are used to identify search methods, both, for SO solutions and for time-dependent tolls and transit fares that preserve the solutions as Nash equilibriums. In every case studied, these prices exist. They must peak concurrently for the two modes in both periods.In special cases involving only one mode, only one period or concentrated demand the solution to the complete problem decomposes by period conditional on the number of transit users, and this facilitates the solution. In these cases the day-long SO cost is the sum of the SO costs for the two peaks considered separately. However, this is not true in general – the solution obtained by combining the two single-period solutions can be infeasible. When this happens, the optimum day-long cost will exceed the sum of the single-period costs. The discrepancy is about 40% of the total schedule penalty for an example representing a large city. Thus, to develop realistic policies the day-long problem must be addressed head on. An approximate method that yields closed form formulas for the case with uniformly distributed wishes is presented.     

Development of a delay model for unsignalized intersections applicable to traffic assignment

Abstract

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models.

Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods.

A comparison between delay models performed using different time intervals shows that the coefficients of determination, R ², increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.     

Signalized intersections with variable flow rates: II A model for delay estimation

This second part of our work develops a model for delay estimation at intersections whose traffic signal controls are continuously being updated. Generally, these traffic signals are centrally controlled. The foundation for the delay estimation model is based on a queuing theory model called “Preemptive resume discipline for M/G/1 with two priority levels.” This queuing model assumes that two customers arrive at acertain point by a Poisson arrival process, and that one customer has service priority over the second customer. The analogy for the case of intersection control is that the preferred customers are the red lights and the secondary customers are the vehicles. In order to adapt the model to the realistic behavior of vehicle traffic at continuously adjusted signals, components are derived to modify the model. The simulation results of the first part of this work are used to calculate adjustment factors that fairly accurately reproduce the simulated delays. This gives rise to the advantage of using in practice a closed mathematical model, in particular when trying to optimize the operation of signalized intersections at the network level.     

Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed

Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.     

A time-series forecast of average daily traffic volume

This paper presents a procedure for forecasting average daily traffic using a time-series analysis. The procedure assumes a logistic function to model traffic volume over a period of years. Model parameters are estimated using ordinary least-squares regression. The method was tested empirically. Model parameters were found to be significant for each of the three different thoroughfares. Further, time-series forecasts compared favorably to observed traffic and to interpolated forecasts for the same period. The method is simpler to and more economical than the standard demand forecasting procedure and is recommended where land-use patterns are stable and only small modifications to the thoroughfare network are planned.     

Estimating link travel time functions for heterogeneous traffic flows on freeways

Oversized vehicles, such as trucks, significantly contribute to traffic delays on freeways. Heterogeneous traffic populations, that is, those consisting of multiple vehicles types, can exhibit more complicated travel behaviors in the operating speed and performance, depending on the traffic volume as well as the proportions of vehicle types. In order to estimate the component travel time functions for heterogeneous traffic flows on a freeway, this study develops a microscopic traffic‐simulation based four‐step method. A piecewise continuous function is proposed for each vehicle type and its parameters are estimated using the traffic data generated by a microscopic traffic simulation model. The illustrated experiments based on VISSIM model indicate that (i) in addition to traffic volume, traffic composition has significant influence on the travel time of vehicles and (ii) the respective estimations for travel time of heterogeneous flows could greatly improve their estimation accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

Stochastic prediction of train delays in real-time using Bayesian networks

In this paper we present a stochastic model for predicting the propagation of train delays based on Bayesian networks. This method can efficiently represent and compute the complex stochastic inference between random variables. Moreover, it allows updating the probability distributions and reducing the uncertainty of future train delays in real time under the assumption that more information continuously becomes available from the monitoring system. The dynamics of a train delay over time and space is presented as a stochastic process that describes the evolution of the time-dependent random variable. This approach is further extended by modelling the interdependence between trains that share the same infrastructure or have a scheduled passenger transfer. The model is applied on a set of historical traffic realisation data from the part of a busy corridor in Sweden. We present the results and analyse the accuracy of predictions as well as the evolution of probability distributions of event delays over time. The presented method is important for making better predictions for train traffic, that are not only based on static, offline collected data, but are able to positively include the dynamic characteristics of the continuously changing delays.     

An applicatoin of an optimal traffic control during lane closure periods of a two‐lane road

On two‐lane roadways, when provision should be made for alternative one‐way movement (for construction or maintenance), the traffic characteristics differ from normal operation in which no lane is closed. The purpose of this study is to derive optimal strategies for controlling, by means of traffic signals, the one lane operation (in two‐way roadways). In this study, strategies to determine the optimal length of the closed one‐lane section during construction and maintenance work are established. This length minimizes the objective function representing the tradeoff between delay cost and operational cost. Using the length value obtained by the proposed model, plus a timing methodology, the overall cost of operating the closed highway section can be minimized. The sensitivity analysis of the optimal solution for the section length is examined with respect to the input variables. This analysis shows that for very low traffic flow values (less than 100 vph per direction), the solution is very sensitive to fuel price changes, while for greater flow values, the solution is insensitive to this change. Similar results are obtained for changes in the worker's wage which is part of both the operational and the delay cost. That is, the section's length is sensitive to wage changes at low traffic flow and insensitive otherwise. Based on the results presented in this study, it is possible to establish a guideline for the section's length determination. The control measure can emerge from a pair of traffic signals that can be installed at both ends of the closed highway segment. This traffic control system is also described in the study in terms of its components and operational features and advantages.     

Values of travel time in Europe: Review and meta-analysis

This paper reports the most extensive meta-analysis of values of time yet conducted, covering 3109 monetary valuations assembled from 389 European studies conducted between 1963 and 2011. It aims to explain how valuations vary across studies, including over time and between countries. In addition to the customary coverage of in-vehicle time in review studies, this paper covers valuations of walk time, wait time, service headway, parking space search time, departure time switching, time in congested traffic, schedule delay early and late, mean lateness and the standard deviation of travel time. Valuations are found to vary with type of time, GDP, distance, journey purpose, mode, the monetary numeraire and a number of factors related to estimation. Model output values of time compare favourably with earnings data, replicate well official recommended values obtained from major national studies, and are transferable across countries. These implied monetary values serve as very useful benchmarks against which new evidence can be assessed and the meta-model provides parameters and values for countries and contexts where there is no other such evidence.     

Flight and passenger delay assignment optimization strategies

This paper compares different optimization strategies for the minimization of flight and passenger delays at two levels: pre-tactical, with on-ground delay at origin, and tactical, with airborne delay close to the destination airport. The optimization model is based on the ground holding problem and uses various cost functions. The scenario considered takes place in a busy European airport and includes realistic values of traffic. A passenger assignment with connections at the hub is modeled. Statistical models are used for passenger and connecting passenger allocation, minimum time required for turnaround and tactical noise; whereas uncertainty is also introduced in the model for tactical noise. Performance of the various optimization processes is presented and compared to ration by schedule results.     

A framework for the assessment of collaborative en route resource allocation strategies

Airspace Flow Programs (AFPs) assign ground delays to flights in order to limit flow through capacity constrained airspace regions. AFPs have been successful in controlling traffic with reasonable delays, but a new program called the Combined Trajectory Options Program, or CTOP, is being explored to further accommodate projected increases in traffic demand. In CTOP, centrally managed rerouting and user preference inputs are also incorporated into initial en route resource allocations. We investigate four alternative versions of resource allocation within CTOP in this research, under differing assumptions about the degree of random variability in airline flight assignment costs when measured against a simple model based upon the flight specific, but otherwise fixed, ratio of airborne flight time and ground delay unit cost. Two en route resource allocation schemes are based on ordered assignments that are similar to those used currently, and the other two are system-optimal assignment schemes. One of these system-optimal schemes is based on complete preference information, which is ideal but not realistic, and the other is based on partial information that may be feasible to implement but yields less efficient assignments. The main contribution of this research is a methodological framework to evaluate and compare these alternative en route resource allocation schemes, under varying assumptions about the information traffic managers have been provided about the flight operators’ route preferences. The framework allows us to evaluate these various schemes under differing assumptions about how well the traffic managers’ flight cost model captures flight costs. A numerical example demonstrates that a sequential resource allocation scheme – where flights are assigned resources in the order in which preference information is submitted – can be more efficient than a scheme that offers a cost minimizing allocation based on less complete preference information, and may at the same time be perceived as equitable. We also find that assigning resources in the order flights are scheduled results in less efficient allocations, but more equitable ones.     

Traffic modelling in system boundary expansion of road pavement life cycle assessment

This paper uses a case study of a UK inter-urban road, to explore the impact of extending the system boundary of road pavement life cycle assessment (LCA) to include increased traffic emissions due to delays during maintenance. Some previous studies have attempted this but have been limited to hypothetical scenarios or simplified traffic modelling, with no validation or sensitivity analysis. In this study, micro-simulation modelling of traffic was used to estimate emissions caused by delays at road works, for several traffic management options. The emissions were compared to those created by the maintenance operation, estimated using an LCA model. In this case study, the extra traffic emissions caused by delays at road works are relatively small, compared to those from the maintenance process, except for hydrocarbon emissions. However, they are generally close to, or above, the materiality threshold recommended in PAS2050 for estimating carbon footprints, and reach 5–10% when traffic flow levels are increased (hypothetically) or when traffic management is imposed outside times of lowest traffic flow. It is recommended, therefore, that emissions due to traffic disruption at road works should be included within the system boundary of road pavement LCA and carbon footprint studies and should be considered in developing guidelines for environmental product declarations of road pavement maintenance products and services.     

Distributed model predictive control for railway traffic management

Every day small delays occur in almost all railway networks. Such small delays are often called “disturbances” in literature. In order to deal with disturbances dispatchers reschedule and reroute trains, or break connections. We call this the railway management problem. In this paper we describe how the railway management problem can be solved using centralized model predictive control (MPC) and we propose several distributed model predictive control (DMPC) methods to solve the railway management problem for entire (national) railway networks. Furthermore, we propose an optimization method to determine a good partitioning of the network in an arbitrary number of sub-networks that is used for the DMPC methods. The DMPC methods are extensively tested in a case study using a model of the Dutch railway network and the trains of the Nederlandse Spoorwegen. From the case study it is clear that the DMPC methods can solve the railway traffic management problem, with the same reduction in delays, much faster than the centralized MPC method.     

Evaluation of green wave policy in real-time railway traffic management

In order to face the expected growth of transport demand in the next years, several new traffic control policies have been proposed and analyzed both to generate timetables and to effectively manage the traffic in real-time. In this paper, a detailed optimization model is used to analyze one such policy, called green wave, which consists in letting trains wait at the stations to avoid speed profile modifications in open corridors. Such policy is expected to be especially effective when the corridors are the bottleneck of the network. However, there is a lack of quantitative studies on the real-time effects of using this policy. To this end, this work shows a comparison of the delays obtained when trains are allowed or not to change their speed profile in open corridors. An extensive computational study is described for two practical dispatching areas of the Dutch railway network.     

A model for evaluating the environmental and functional benefits of “innovative” roundabouts

This study looks at the singling out of a multi-parameter criterion for choosing conventional or innovative roundabout layouts, by taking functional, environmental and economic aspects into consideration. The performances of three conventional roundabouts (with different lane number at entries and through the ring), turbo-roundabouts and roundabouts with right-turn bypass lane on all the arms (flower roundabouts) have been compared in terms of vehicle delays and pollutant (carbon dioxide, nitrogen oxides, particle pollution (PM₁₀ and PM_2.5)) emissions. By means of closed-form capacity models and with the help of COPERT IV^© software, several traffic simulations have been carried out, referred to yearly peak flow values Q_max and ranging between 1300 and 3300 veh/h, starting from a typical annual traffic demand curve in urban areas. The estimation of cumulative vehicle delays and annual pollutant emissions, together with construction and maintenance costs has allowed working out overall costs for each roundabout under consideration, depending on the traffic demand. Thus, the proposed model allows finding the most cost-effective geometric solution as to overall costs for a comprehensive case record of traffic values.     

Characterization and prediction of air traffic delays

This paper presents a new class of models for predicting air traffic delays. The proposed models consider both temporal and spatial (that is, network) delay states as explanatory variables, and use Random Forest algorithms to predict departure delays 2–24 h in the future. In addition to local delay variables that describe the arrival or departure delay states of the most influential airports and links (origin–destination pairs) in the network, new network delay variables that characterize the global delay state of the entire National Airspace System at the time of prediction are proposed. The paper analyzes the performance of the proposed prediction models in both classifying delays as above or below a certain threshold, as well as predicting delay values. The models are trained and validated on operational data from 2007 and 2008, and are evaluated using the 100 most-delayed links in the system. The results show that for a 2-h forecast horizon, the average test error over these 100 links is 19% when classifying delays as above or below 60 min. Similarly, the average over these 100 links of the median test error is found to be 21 min when predicting departure delays for a 2-h forecast horizon. The effects of changes in the classification threshold and forecast horizon on prediction performance are studied.     

A Heuristic Algorithm for the Allocation of Airport Runway System Capacity

Abstract

This paper develops a heuristic algorithm for the allocation of airport runway capacity to minimise the cost of arrival and departure aircraft/flight delays. The algorithm is developed as a potential alternative to optimisation models based on linear and integer programming. The algorithm is based on heuristic (‘greedy’) criteria that closely reflect the ‘rules of thumb’ used by air traffic controllers. Using inputs such as arrival and departure demand, airport runway system capacity envelopes and cost of aircraft/flight delays, the main output minimises the cost of arrival and departure delays as well as the corresponding interdependent airport runway system arrival and departure capacity allocation. The algorithm is applied to traffic scenarios at three busy US airports. The results are used to validate the performance of the proposed heuristic algorithm against results from selected benchmarking optimisation models.