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A feasibility study has been made of the planned railway connection to the mainland Russian Federation and Sakhalin Island based on a comparative analysis of currently available data and calculated characteristics obtained in the course of this study. The research was carried out in the following areas: comparative analysis of cargo transportation along the Vladivostok–Kholmsk route, calculation of payback based on the most modern reporting data, and a comparative analysis of historical projects, recent government documents and existing technical projects. Data of a comparative analysis of cargo transportation in terms of three parameters (distance, travel time, and volume of cargo transportation) of the existing railway-ferry service and the planned railway connection along the Vladivostok–Kholmsk route, taking the characteristics of the ferry service into account, were obtained and used as a basis to draw economic-geographical conclusions. A calculation of the recoupment of the planned railway connection was done according to average statistical information. A comparative analysis was made of the historical projects beginning in 1875, as well as an analysis of recent state documents (plans, strategies, and programs). A comparative analysis was made of the current modern technical projects from the bridge-tunnel alternative and their advantages and disadvantages; on its basis, a technical conclusion was made about the possibility and prospects of construction.

Passengers on metro platforms can board a train only when the train has surplus capacity and the dwell time is sufficient, while the latter condition is omitted in previous studies. Taking into account the impacts of train capacity and dwell time on passengers boarding, this study develops a model on optimizing metro timetable to reduce passenger travel time and metro operating cost, through regulating trains’ inter-station run-time, dwell time and headway. The NSGA-II algorithm is employed to obtain the near-optimal Pareto Frontier of the proposed model. To address insufficient dwell time scheduled in the timetable, three operating strategies are proposed and compared: a. sticking to nominal timetable; b. extending dwell time only; c. extending dwell time and recovering delay as soon as possible by compressing train inter-station run-time. Case studies on real-life metro line prove that some passengers cannot board the train during peak hours due to insufficient dwell time. In this context, strategy a brings low-quality service because passengers are stranded at platform even though the train has surplus capacity. In contrast, more passengers can board the train with strategies b and c because dwell time is extended for passengers’ boarding when train has surplus capacity. Compared to strategy b, strategy c reduces the average in-vehicle time of passengers by 2.5% through compressing inter-station run-time to recover the delay. The timetable optimized based on strategy c saves total travel time of passengers by 3.1% without increasing operating cost when compared to the practical timetable.

Small fast passenger boats serve to carry passengers at certain routes particularly in short distance between the islands. The passengers preferred those transport modes due to its short travel time. In fact, there are many accidents occur during boat operation which end up with the loss of life and materials at sea. An intensive study was executed by the authors with the purpose to obtain an unsinkable boat. The boat data of existing boats were collected and be used for re-designing process. During the design process, some solid boxes, expanded bags were provided inside boat to reduce the incoming water. Meanwhile, the bags were fitted outside the boat (side floater) to provide additional buoyancy and righting moment for stability performance. A boat model was developed and tested to confirm the design results. In addition, a full-scale boat was developed and equipped with solid boxes, air bags and side floaters. The theoretical computation and extrapolated results from model proved that the required volume for inside solid boxes and air bags are 1.213 m3 and 0.511 m3 respectively. Meanwhile, the required volume of outside air bags is 0.357 m3. It was found that the total boat weight, passengers and incoming water of 4.259 tons are balanced by weight displacement of 5.025 tons. There was reserve buoyancy of 0.766 tons that supports the boat to be float and side floaters to prevent the boat from capsize. The result of sea trial showed that with all loading conditions the boat was still afloat (unsinkable).

The aim of this study is to understand the collective movements of individuals and to observe how individuals interact within a physical environment in a crowd dynamic, which has drawn the attention of many researchers. We conducted an experimental study to observe interactions in the collective motions of people and to identify characteristics of pedestrians when passing obstacles of different sizes (bar-shaped, 1.2 m, 2.4 m, 3.6 m and 4.8 m), going through one narrow exit and employing three different flow rates in walking and running conditions. According to the results of our study, there were no differences in collision-avoidance behaviour of pedestrians when walking or running. The pedestrians reacted early to the obstacles and changed the direction in which they were walking by quickly turning to the left or to the right. In terms of the speed of the pedestrians, the average velocity was significantly affected while performing these tasks, decreasing as the size of the obstacle increased; therefore, the size of obstacles will affect flow and speed levels. Travel time was shorter when participants were in the medium-flow rate experiments. In terms of the distance of each individual’s travel, our data showed that there was no significant difference in all the flow rate experiments for both speed levels. Our results also show that when the pedestrians crossed an obstacle, the lateral distance averaged from 0.3 m to 0.7 m, depending on the flow rate and speed level. We then explored how the body sways behaved while avoiding obstacles. It is observed that the average sway of the body was less in the high-speed conditions compared to the low-speed conditions – except for the HF & 4.8 m experiment. These results are expected to provide an insight into the characteristics of the behaviour of pedestrians when avoiding objects, and this could help enhance agent-based models.

A wide range of disruptive events occur on transportation networks which have intense impacts on network users’ travel time. Moreover, limited funds is forcing national, regional and local governments to carefully prioritize their investments. Therefore, reliable quantitative tools are needed to help decision-makers in choosing their investments so that the allocation of available resources is optimized. In this research, the authors attempt to understand the relative importance of links in a road network and suggest a methodology to rank the links according to three importance factors while combining the network improvement investment decision and subsequent network user response in a feedback loop. The first factor is based on the link flows at equilibrium. The second factor is based on the importance of facilities served. The third factor is based on the number of origin-destination pairs served by a link. The proposed methodology is demonstrated with a small test network and with real scale transportation network. Sensitivity analysis is performed using various budget scenarios and it is found that with the increase in budget the ranking of critical links changes.

A large amount of information is required to model the complex trade-off processes between travel activities, non-travel activities and budget assignment at the individual level. This paper describes the development of a new survey design, which incorporates components of travel surveys, time use surveys and consumer expenditure surveys in an integrated format, which is expected to deliver a richer data set allowing deeper insights into individuals' activity and consumption patterns. The survey procedure and the incentives paid, which were necessary to obtain acceptable response rates, are also described. Results from two pilot studies using a trip-based and an activity-based diary format are presented. The paper examines to which extent the diaries have been capable of collecting the required data with high quality and response rates. The innovative "Mobility-Activity-Expenditure-Diary" is introduced and results of the main survey using this design are presented. Travel behaviour and non-travel activities were reported at high quality. Expenditures would require longer observation periods (and preferably not only telephone but also personal support in the survey process) to reduce unsystematic variations and to better capture individuals' long term equilibrium.

Travel-based multitasking, i.e. using travel time to conduct enjoyable and/or productive activities, is the subject of an increasing number of theoretical and empirical studies. Most existing studies focus on modelling the choice of which activities people conduct while travelling, and a limited number of papers also focuses on their duration. The novelty of this study with respect to this literature is two-fold. Firstly, we specifically study the engagement in different online activities while travelling, and apply the state-of-the-art Multiple Discrete-Continuous Extreme Value (MDCEV) model to jointly model the choice and duration of multiple activities. We apply this model to data collected face-to-face from train passengers in the UK. We find that activity choice and duration is explained by both passenger and trip characteristics, especially trip purpose, ticket type and day/time of the trip. Secondly, we show how such modelling can assist in investment appraisal, in particular by providing insights into lower- and upper- bound estimates of the proportion of the entire travel time spent working, itself of importance in, for example, valuation of business travel time using the so-called Hensher Equation. We present a detailed discussion of how the findings from our work contribute to the broader discourse around the nature of travel time and its valuation.

Aluminum 6061-T6 alloy of thickness 3mm is friction welded by this experimental examination. The rotational speed of the tool is varied like 560, 900, and, 1400rpm. The feed is selected as 20, 63, and, 100mm/min. The tilt angle is kept at 0 degree. The welded joints are produced by taking different welding angle such as 0-, 30- and 60- degrees. The tool steel pin having a circular and hexagonal profile is used to produce the welded joints. The tensile samples are extricated from the welded specimens and followed for the testing. The maximum tensile strength is observed like 213MPa, 256MPa, and 200MPa at 0-, 30-, and 60-degrees welding angles respectively with the use of a circular profiled probe. The highest tensile strength has arrived like 191MPa, 207MPa, and 212MPa at 0-, 30-, and 60-degrees welding angles respectively with the use of a hexagonal profiled probe. The weld strength is observed superior at the lower rotating speed of the tool but welding travel time is observed to be more. The improvement in hardness value is observed in relevance with the increase in tool rotating speed. The welding process is noticed to be productive and qualitative for the welding of aluminum and can be used for various industrial applications.

Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.

Until now the use of concrete as a building material is still widely used for building structures. It is important to do concrete compressive strength testing as one of the factors to know the quality of a concrete. NDT (Non - Destructive Testing) is a method of solid quality testing without damaging the object. Testing with the NDT method is considered more efficient than the destructive test method. One method for performing NDT testing is by utilizing UPV (Ultrasonic Pulse Velocity). UPV is a method for estimating concrete compressive strength based on the ultrasonic pulse velocity relationship through concrete with the concrete compressive strength itself. UPV testing works by emitting ultrasonic pulses of 40 kHz through concrete to obtain the travel time of the pulse . Then the resulting time will be calculated the value of its speed and then will be converted into concrete compressive strength. Concrete compressive strength measurement system for high - quality concrete using UPV method can be designed by utilizing relation between ultrasonic pulse velocity with concrete compressive strength. Based on the test results, the average error value of concrete compressive strength testing is 3.04% with a maximum error of 6.63%.

Accessing airports has been a problem for passengers due to traffic congestion in cities. Thus, in order to catch the flights on time, passengers consider many factors to choose the transportation mode for airport access. Some of the factors are covariates, and they can be listed as age of the passenger, number of luggage carried, travel cost and travel time to the airport, group size of the passengers, and reliability of access modes. These covariates, may differ for each airport; and hence, in this paper, we investigated the differences of these covariates between Istanbul Ataturk International Airport (IST) and Istanbul Sabiha Gokcen International Airport (SAW). Analysis of Variance (ANOVA) and post-hoc tests were conducted to investigate the covariates that affect the mode choice to access IST and SAW. The results indicated that these two airports have differences in terms of the effect of these selected six covariates on airport access mode choice. Reliability of the modes, travel time, travel cost, age of the passenger and traveling group size affected the mode choice to access IST. On the other hand, only reliability of the modes, travel time and travel cost had effects on airport access mode choice for SAW. Based on the analysis, some recommendations for the decision makers were also provided. Şehirlerde trafik sıkışıklığından dolayı havalimanlarına erişim bir sorun haline gelmiştir. Bu yüzden, yolcular havalimanlarına erişimdeki ulaşım türü seçimlerinde uçuşlarını kaçırmamak adına birçok faktörü değerlendirmektedir. Bu faktörlerin bazısı yolcunun yaşı, beraber seyahat ettiği kişi sayısı, bagaj sayısı, havalimanına yaptığı seyahatin maliyeti ve süresi ile ulaşım türünün güvenirliğidir. Bu faktörler havalimanları arasında farklılık gösterebilir. Dolayısıyla, bu çalışmada, sözü edilen faktörlerin farkı İstanbul Atatürk Uluslararası Havalimanı (IST) ve İstanbul Sabiha Gökçen Uluslararası Havalimanı (SAW) arasında incelenmiştir. Bu inceleme, Varyans Analizi (ANOVA) ve ona ait post-hoc testleri ile yapılmıştır. Sonuçlara göre, havalimanına erişimde kullanılan ulaşım türlerinin seçimini etkileyen faktörler hususunda bu iki havalimanı arasında farklılıklar mevcuttur. Ulaşım türlerinin güvenirliliğinin, seyahat süresinin, seyahat maliyetinin, yolcunun yaşının ve yolcuyla beraber seyahat eden kişi sayısının, IST’a erişim için ulaşım tür seçiminde etkili olduğu ortaya çıkmıştır. Ancak SAW’a erişim için sadece ulaşım türü güvenirliliği, seyahat süresi ve seyahat maliyeti tür seçiminde etkin olmuştur. Analizin sonuçlarına bağlı olarak, karar verici merciiler için bazı tavsiyelerde bulunulmuştur.

Shallow aquifers of coastal and internal alluvial plains of developed countries are commonly characterized by the challenging management of groundwater resources due to the intense agricultural and industrial activities that determine a high risk of groundwater contamination. Among the principal origins of pollution in these areas are agricultural practices based on the amendment of soils by nitrate fertilizers, which have been recognized as one of the most severe environmental emergencies for which specific policies and regulations have been issued (e.g., EU Directive 2006/118/EC). In such a framework, the results of research aimed at assessing the specific vulnerability of shallow alluvial aquifers to nitrate fertilizer pollutants by coupled process-based and empirical approaches are here proposed. The research focused on assessing the specific vulnerability to nitrate pollution of a shallow alluvial aquifer of the Campania region (southern Italy), which was selected due to its representativeness to other recurrent hydrogeological settings occurring in alluvial plains of the region and worldwide. In this area, 1D hydro-stratigraphic models of the unsaturated zone were reconstructed and applied for simulating the transport of nitrate pollutants at the water table and estimating the associated travel times. Numerical modeling was carried out by the finite differences VS2TDI code and considered a 10-year time series of rainfall and evapotranspiration as well as typical local farming practices of nitrate fertilizer input. Results of the travel time calculated for the 1D hydro-stratigraphic models considered and at different depths were recognized as a proxy to assess the specific vulnerability to nitrate fertilizer pollution. Among the principal outcomes is an empirical multiple correlation between the travel time of the nitrate fertilizer pollutant, water table depth, and equivalent saturated hydraulic conductivity of the unsaturated zone or hydraulic resistance, which was used to assess the travel time at the distributed scale over the whole area studied as well as the related specific vulnerability. Given such results, the coupled process-based and empirical approach is proposed as generally applicable for assessing and mapping groundwater vulnerability in shallow aquifers, for which detailed stratigraphic and piezometric data are available.

We provide a novel methodology for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. We also provide an estimate of the travel time associated with this path. Lagrangian pathways and travel times are of practical value not just in understanding surface velocities, but also in modelling the transport of ocean-borne species such as planktonic organisms, and floating debris such as plastics. In particular, the estimated travel time can be used to compute an estimated Lagrangian distance, which is often more informative than Euclidean distance in understanding connectivity between locations. Our methodology is purely data-driven, and requires no simulations of drifter trajectories, in contrast to existing approaches. Our method scales globally and can simultaneously handle multiple locations in the ocean. Furthermore, we provide estimates of the error and uncertainty associated with both the most likely path and the associated travel time., Comment: 27 pages, 10 figures in the main text. 13 pages, 8 figures in the supplemental material

The reliable simulation of pedestrian movement is an essential tool for the security aware design and analysis of buildings and infrastructure. We developed HyDEFS, an event-driven dynamic flow simulation software which is designed to simulate pedestrian movement depending on varying routing decisions of the individual users and varying constraints. HyDEFS uses given density depending velocities to model congestions and evaluates flow distributions with respect to average and maximum travel time. This is of particular importance when considering evacuation scenarios. We apply HyDEFS on two small networks and cross validate its results by time-discrete and time-continuous calculations.

With the advances in sensing technologies along with innovative modelling and estimation method, a variety of Intelligent Transportation Systems have been developed to mitigate traffic congestion and associated environment pollution problems. In recent decade, more advanced sensing technologies have been invented for dedicated measurements. In this dissertation, we studied traffic activity estimation algorithm with three types of advanced sensing systems: 1) sparse mobile sensors for arterial travel time estimation; 2) wireless magnetic sensors for arterial energy/emission estimation; and 3) 3-D lidar for lane-level vehicle trajectory estimation.Due to the interruption by traffic signals, it becomes very challenging to estimate average travel time of traffic flows along the signalized arterial by using conventional inductive loop detectors (ILD). Position samples from fast-growing smart phone and commercial navigator technologies turn out to be another promising data source for this task. However, one of the major obstacles of using these technologies is the randomness of sampling location, which results in significant variation in measured distance between two consecutive samples, compared to the ILD technology. In Chapter 3, we developed a novel probabilistic travel time model to deal with this issue by decomposing the arterial travel time into two components: free-flow travel time and delayed time. Validated by field operational tests, the proposed model has exhibited a good fit on the travel time distribution under different congestion levels and has resulted in more reliable and robust vehicle's activity classification to differentiate stopped and free flow maneuvers by each individual vehicle. With the second benefit, we developed an arterial energy/emission estimation approach in Chapter 4, using wireless magnetic sensors which measure travel time directly for each re-identified vehicle. An approximated speed trajectory is then reconstructed for stopped vehicles and fed into a microscopic energy/emissions model to achieve more accurate energy/emissions estimation.Lane-level second-by-second vehicle trajectory is another important data source, which is particularly useful for traffic simulators to calibrate the internal vehicle activity models. In Chapter 5, we present a novel mobile sensor platform consisting of centimetre-level accurate positioning system and 3-D lidar for surrounding vehicle trajectory collection. A robust detection/ tracking algorithm has been developed to extract a large number of trajectories from vehicles surrounding the sensor-equipped probe vehicle. Results from both freeway and arterial have shown great potentials of such innovative sensing systems in building high quality trajectory repository for future research.

In 1973, a three-part full-page article was published in the local newspaper describing our society in the region of Twente, the Netherlands, in 1923, 1973, and 2023 [1]. The author of this article was only 11 years old at the time, but the predictions made were interesting enough to keep that article for 45 years. The article [1] predicted several innovations for 2023, 50 years ahead, such as a magnetic levitation monorail from Twente to Amsterdam reducing the travel time from 2 hours to less than 30 minutes, as shown in Figure 1. However, all we got after 45 years was a new motorway, with lots of traffic jams, and a train that still takes 2 hours. Despite over a century of research and development, maglev trains are currently operated in just three countries: Japan, South Korea, and China. Other innovations predicted in 1973 for 2023 were clothes made by printers (yes, nearly), no cars in city centres (yes), products made in Shanghai (yes), European regions instead of European countries (no, although Catalonia would like this), no manufacturing but only research and development (no), Holec and Signaal merged (no, Holec split up into several divisions now owned by Siemens, Eaton, and General Electric, and Signaal was sold by Philips to Thomson-CSF, now THALES). And the announcement that the University of Twente will be organizing a conference on communication technology, including electronics. This was a very good prediction: communication technology will be a key issue in 2023.

Over the past few years, rapid urbanization has stressed an urgent need for the development of comprehensive and efficient contingency plans in urban spaces. Amongst other actions to take, evacuation plans are important solutions to protect people from unforeseen effects of disasters. In this paper, we develop an optimal method to design appropriate evacuation plans. Its objectives include minimizing total clearance time and travel time of each evacuee, avoiding traffic congestions, and balancing traffic loads among different exits. A series of algorithms are developed to determine the departure time, the destination, and the route of each evacuation group. A time-extended network model is presented to support these algorithms. A real-world case is employed to examine the performance of the proposed model. The performance and applicability of the proposed approach when targeting different scales and granularities of evacuation are discussed and analyzed in detail. Results demonstrate that the above four objectives can be closely satisfied, and the approach performs better for evacuations with a large scale and a small granularity.

The objective of this study is to examine travel time reliability under the heterogeneous traffic conditions prevailing in developing countries such as India. The study is carried out using a motorised two-wheeler, which is a dominant vehicular type in conditions such as are found in India on urban arterial roads. Eight runs are conducted during morning peak hours by driving a motorised two-wheeler mounted with a performance box (equipped with high-precision global positioning system) with the traffic stream on the test route between Athwa and Piplod, in Surat city, Gujarat, situated in the western part of India. One of the major outcomes of the study is to propose the most suitable relationships between buffer time index (BTI) and volume-demand-to-capacity (V/C) ratio, as well as between planning time index (PTI) and V/C ratio. From the results, it is found that a polynomial function-based model can predict BTI as well as PTI for the given V/C ratio more accurately. The study also highlights the development of a methodology to define the level of service of a roadway facility based on travel time reliability measures. Furthermore, this research work discusses potential reliability measures for capturing travel time variations more appropriately.

As the exploitation of an open-pit mine is evolving, the depth and therefore the distance traveled by the mine haul trucks increases. As a result, the operational cost of mining operation increases due to the higher consumption of fuel and tires. This work presents the evaluation of the performance of a truck fleet when using a trolley assist system. A model of the trolley system is developed using an active-front-end converter, which in combination with the integrated electric-mechanical model of a mining truck, allows quantifying the production increase, fuel savings, and net energy consumption during an up-down cycle of the truck. A set of evaluations are presented, first on idealized uphill and downhill routes, and then using the coordinates of a route of a copper mine located to the north of Chile. Results show a 44% increase in the upgrade speed, a 16% reduction of the travel time, and a fuel savings of over 85% on each up-down cycle. The tool developed allows evaluating the performance of a truck fleet in any mine facility just knowing the coordinates of the mine routes and the models of the trucks.

Various intelligent transportation systems and strategies for reducing vehicle fuel consumption and emissions have been developed. Energy and emissions could be reduced with the compromise of travel time in some environment-focused Connected Vehicle (CV) applications, which highlighted performance measures in terms of mobility and sustainability. Nevertheless, few studies have focused on safety assessment of such CV applications. In this study, a CV-based eco-driving application, namely, Eco-Approach and Departure (EAD), is selected as an example. A microscopic safety analysis tool, Surrogate Safety Assessment Model (SSAM), is utilized to assess the safety influence of EAD application in multiple scenarios. Further analysis is performed from two perspectives: (1) application users, i.e., EAD-equipped vehicles versus unequipped vehicles; and (2) traffic operators, i.e., overall traffic performance with and without the introduction of EAD. For each perspective, conflict statistics (e.g., frequency, time-to-collision distribution), overall and by type, are analyzed. Results indicate EAD is beneficial in improving the safety performance of equipped vehicles. The influence of EAD on overall traffic is scenario dependent, and a high penetration rate shows positive effects on network-wide safety benefits for most scenarios.

A number of full service network carriers have recently stated their ambition to develop certain ultra-long-range (ULR) routes, such as Doha to Auckland, Dubai to Auckland, Dubai to Panama City, Singapore to San Francisco, Singapore to New York, all of which require a great circle distance between 7,000–9,000 nautical miles (nm) with an estimated travel time between 15 and 20 hours. This paper examines the capability of the current generation of wide-bodied passenger aircraft to satisfy this evolving strategy, and the impact, if any, on the provision of air cargo transportation. An exploratory study is presented herein based on an assessment of each aircraft type’s payload-range envelope, taken from the appropriate Aircraft Airports Handling Characteristics Manual. The key findings reveal that airlines wishing to pursue this ultra-long-range strategy have a surprisingly limited choice of current-generation passenger aircraft which are capable of flying the desired mission profile without compromising significantly on passenger numbers and cargo payload.

This paper proposes a method to measure the value, forecast, and incorporate reliability in the transportation planning process. Empirically observed travel time data from INRIX are used in an introduced method to measure Origin Destination (OD)-based reliability. OD-based reliability is a valuable concept, since it can be easily incorporated in most travel models. The measured reliability is utilized to find the value of reliability for a specific mode choice problem and to establish the relationship between travel time and reliability. This relationship is useful to forecast reliability in future scenarios. Findings are combined with Maryland Statewide Transportation Model to find the value of reliability savings by improving the network in a case study. The Inter-County Connector is used as the case study to show the significance of reliability savings. The proposed approach can be used to (1) provide a systematic approach to estimate travel time reliability for planning agencies, (2) incorporate travel time reliability in transportation planning models, and (3) evaluate reliability improvements gained from transportation network investments.

Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.

The main objective of this research is to select the best site for the establishment of a new bridge on the Al Gharraf river within the limits of the Al Muwaffaqiyah region . Al Muwaffaqiyah region is located in Wasit province in Iraq. The study area is divided into multiple zones . Three alternatives are proposed to construct the bridge site. The best one is selected using network analyst tool of ArcGIS software depending on the largest number of routes passing between zones. The route link between both sides of Al Gharraf river in Al Muwaffaqiyah region is one of the important projects in the city. The project will provide a new crossing point on Al Gharraf river . The proposed route will contribute to reduce traffic congestion depending on the foundations of planning and traffic so that gives the desired result of this project in terms of the location and importance of economic feasibility.

Since high-speed rail (HSR) is designed primarily to connect large cities, it challenges how smaller cities en-route are still going to be serviced by rail. Scholars have focused mainly on cities bypassed by HSR that have experienced a decrease in conventional rail services or on how several smaller cities have nevertheless been able to secure appropriate facilities to be served by high-speed trains in the context of compromises between HSR travel time and political pressures. Indeed, local and regional authorities often do their best to secure specific rail infrastructures to accommodate HSR services. However, in their euphoria they usually forget to consider HSR operations. Yet it is the services supplied (routes, frequencies and timetables) that ultimately determine the utility of HSR for those smaller cities, and the real possibility of being connected to other cities. In this context, this paper complements the existing literature by revisiting the case for smaller en-route HSR cities through a service-oriented perspective. We examine four European case studies and find that cities that initially succeed in securing HSR infrastructure may still be bypassed to some extent. Reasons include intermodal competition based on travel time, insufficient potential markets for train companies seeking higher revenues and rail stations not being designed appropriately.

The front velocity of transient ceiling becomes an important consideration for tunnel fire safety, which provide a prediction of travel time of ceiling jet. The previous model predictions of ceiling jet front velocity were developed in a natural ventilation tunnel. The effect of central mechanical exhaust system on the ceiling jet front velocity have not been revealed in the past. This work experimentally revealed smoke front velocity of ceiling jet in a tunnel under the effect of central mechanical exhaust system, and the relative location between fire source and central exhaust system were considered. It is found that, the relative direction of fire source and ceiling mechanical extraction opening has a great influence on the smoke front velocity of ceiling jet. Some previous models (Jones, Benjamin, and CFAST) all overestimate the smoke flow velocity of ceiling front; the predicted ceiling jet front velocity deduced by Jones and Benjamin have the maximum excessive deviation of about 60% and 98%, respectively, but the CFAST model is closer to the measurements with relatively higher front velocity. The Hinkley’s model can be well used to predict it with different central mechanical exhaust system.

This paper presents a complementarity equilibrium model for electric vehicles (EVs). Under the equilibrium conditions, each EV takes the path that is shortest and does not violate the driving range. When the driving range has to be violated, the EVs are allowed to choose a path with a charging station to extend their driving range. To find the shortest such path, a constrained shortest path problem with replenishment (CSPP with replenishment) is formulated that considers the driving range limit of EVs. The CSPP is solved with a label-correcting algorithm with two additional steps that substantially reduce the computation time and the required memory. The first procedure is a pruning technique that eliminates exploring branches (of an enumeration tree) that can no longer become incumbent and the second procedure is an indexing technique that works as a pointer for navigating the generated (enumeration) tree when it becomes too large. Numerical experiments on a number of networks show a substantially lower computation time compared to existing algorithms and the results provide several insights into the driving patterns of EVs. When charging time is increased, the EVs shift to paths that have a longer travel time but a shorter distance. Hence, the total network distance decreases but the total network travel time increases. We also show that unregulated expansion of the charging infrastructure can actually increase the total network travel time due to the presence of Braess’ paradox.

This paper presents results of an I-95 Corridor Coalition sponsored project to assess the ability of outsourced vehicle probe data to provide accurate travel time on signalized roadways for the purposes of real-time operations as well as performance measures. The quality of outsourced probe data on freeways has led many departments of transportation to consider such data for arterial performance monitoring. From April 2013 through June of 2014, the University of Maryland Center for Advanced Transportation Technology gathered travel times from several arterial corridors within the mid-Atlantic region using Bluetooth traffic monitoring (BTM) equipment, and compared these travel times with the data reported to the I95 Vehicle Probe Project (VPP) from an outsourced probe data vendor. The analysis consisted of several methodologies: (1) A traditional analysis using precision and bias speed metrics. (2) A slowdown analysis which quantified the percentage of significant traffic disruptions accurately cap...

The prediction of bus arrival time is important for passengers who want to determine their departure time and reduce anxiety at bus stops that lack timetables. The random forests based on the near neighbor (RFNN) method is proposed in this article to predict bus travel time, which has been calibrated and validated with real-world data. A case study with two bus routes is conducted, and the proposed RFNN is compared with four methods: linear regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), and classic random forest (RF). The results indicate that the proposed model achieves high accuracy. That is, one bus route has the results of 13.65 mean absolute error (MAE), 6.90% mean absolute percentage error (MAPE), 26.37 root mean squared error (RMSE) and 13.77 (MAE), 7.58% (MAPE), 29.01 (RMSE), respectively. RFNN has a longer computation time of 44,301 seconds for a data set with 14,182 data. The proposed method can be optimized by the technology of parallel computing and can be applied to real-time prediction.

Beijing is experiencing severe over-use of its metro system, as is the case in many large cities worldwide. Bicycle can be a competitive mode for short-distance intra-urban trips. In this paper, we explore whether it is possible to use bicycle to replace a portion of short-distance public transport trips. We collected data on bicycle choices and perceptions of bicycling environments. Participants are current metro, bus riders, and bicyclists from 8 short-distance travel scenarios which consisted of 24 study sites across Beijing. Multivariate binary logistic regression models were applied to test association between the perceived bicycling environment and bicycle choices of current metro riders, bus riders, and bicyclists. We found the highest potential for bicycle replacement was for travel distance equivalent to two stations, approximately 1.5km–2 km, with 13% of current metro riders and 10% of bus riders willing to switch. Several perceptual aspects of the bicycle and bicycling environments were revealed that influence intentions to shift to bicycling, including desires for a separated cycle lane, worries about exposure to air pollution and better travel time control. Also, we found there were clear differences between habitual bicyclists and those bicycling occasionally or never.

This paper introduces a simple but practical approach that uses both loop detector data and probe travel times for computing the vehicle hours traveled (VHT), average delay, and level of service (LOS) for signalized intersections. The goal is to improve upon the state-of-the-practice method outlined in the highway capacity manual (HCM) by incorporating additional travel time measurements from probe vehicles or vehicle re-identification systems. The proposed methodology is designed to work under a variety of traffic conditions, including states of congestion in which the HCM methodology is not reliable. Our analysis is then tested using simulation of an arterial site in Arcadia, CA, USA. The results suggest that the proposed methodology performs better at the approach level than at the lane group level. Population size and probe penetration rate are two key parameters in the estimation. Either a large population size or a high penetration rate is required in order to produce reliable estimates of VHT, delay, and LOS. Results also show that the proposed methodology only requires 7% of the penetration rate to outperform the HCM methodology.

Efficient insertion heuristic algorithms allowing multi trips per vehicle (EIH-MT) and allowing a single trip per vehicle with post-processing greedy heuristic (EIH-ST-GH) are proposed to solve the multi-trip inventory routing problem with time windows, shift time limit and variable delivery time (MTIRPTW-STL-VDT) with short planning horizon. The proposed algorithms are developed based on an original algorithm with two enhancements. First, the delivery volumes, the associated beginning delivery times and the exact profits are calculated and maintained. Second, the process to finalize a best-objective and feasible solution is developed. These algorithms are shown to have the complexity of O(n4). These heuristics maximize the profit function, which is the weighted summation of total delivery volume and negative total travel time. EIH-MT and EIH-ST-GH are performed on 280 instances based on Solomon’s test problems with three weight sets. Best-objective solutions are examined to illustrate the feasibility of various constraints. The trade-offs between total delivery volume and total travel time are observed when varying weight values. There is not a single winner heuristic based on the number-of-vehicles, profit and CPU criteria across the three customer configuration types. On average performance, EIH-ST-GH is preferred over EIH-MT for cluster configuration type with the following average improvement percentages: 1.03% for profit, 2.93% for number-of-vehicles and 38.68% for CPU. For random and random-cluster configuration types, EIH-ST-GH should be preferred because of better profit (0.27% for random and 0.22% for random-cluster) and CPU (46.96% for random and 44.06% for random-cluster) improvements. In the comparison of the multi-trip algorithms against the single-trip algorithm, the benefits in reducing the number of vehicles on-average are shown across all customer configuration types.

Variable speed limit (VSL) and ramp metering (RM) affect freeway traffic operations in different ways and, accordingly, result in different effects on system travel time. The primary object...

In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit flow characteristics. Within this framework of constrained optimal boundary flow gating, two main performance metrics are considered: (a) first, connected to the NFD, the concept of average network travel time and delay as a performance metric is defined; (b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled inflow demands. External queue capacities in terms of finite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network’s aggregated behavior. By controlling the number of vehicles joining the internal queue from the external ones, herewith a network traffic flow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically efficiently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satisfied. Comparative numerical simulation studies on a microscopic traffic simulator are carried out to show the benefits of the proposed method.

Abrupt airport outages can cause diversions and fuel-critical situations for flights, leading to costly passenger misconnections. We develop a large neighborhood search heuristic to optimize the rerouting of flights bound for a disrupted airport to a hub airport that is not disrupted, with the goal of accommodating passengers on existing flights departing the non-disrupted hub. The objective of the heuristic is to identify and reroute flights to the ad-hoc hub(s) – non-disrupted hub airport(s) – that minimize the sum of passenger travel time and wait time. We minimize the passenger cost as the sum of passenger travel time to the diversion airport and wait time for a connecting flight at the ad-hoc hub airport, subject to on-board fuel and diversion airport capacity constraints. We use the heuristic to determine how a coordinated traffic management strategy could have diverted flights immediately following a real-world airport outage.

Battery electric buses with zero tailpipe emissions have great potential in improving environmental sustainability and livability of urban areas. However, the problems of high cost and limited range associated with on-board batteries have substantially limited the popularity of battery electric buses. The technology of dynamic wireless power transfer (DWPT), which provides bus operators with the ability to charge buses while in motion, may be able to effectively alleviate the drawbacks of electric buses. In this paper, we address the problem of simultaneously selecting the optimal location of the DWPT facilities and designing the optimal battery sizes of electric buses for a DWPT electric bus system. The problem is first constructed as a deterministic model in which the uncertainty of energy consumption and travel time of electric buses is ignored. The methodology of robust optimization (RO) is then adopted to address the uncertainty of energy consumption and travel time. The affinely adjustable robust counterpart (AARC) of the deterministic model is developed, and its equivalent tractable mathematical programming is derived. Both the deterministic model and the robust model are demonstrated with a real-world bus system. The results demonstrate that the proposed deterministic model can effectively determine the allocation of DWPT facilities and the battery sizes of electric buses for a DWPT electric bus system; and the robust model can further provide optimal designs that are robust against the uncertainty of energy consumption and travel time for electric buses.

This paper explores at the planning level the benefits of coordinating tram movements and signal timings at controlled intersections. Although trams may have dedicated travel lanes, they mostly operate in a mixed traffic environment at intersections. To ensure tram progression, pre-set signal timings at intersections are adjusted by activating Transit Signal Priority (TSP) actions, which inevitably add delays to the auto traffic. A mixed integer program is proposed for jointly determining tram schedules for a single tram line and modifying signal timings at major controlled intersections. The objective is to minimize the weighted sum of the total tram travel time and TSP’s negative impacts on other traffic. A real-world case study of Line 5 of the Shenyang Hunnan Modern Tramway shows that by extending the dwell time or link travel time we can significantly reduce the TSP’s negative impacts on the auto traffic while only slightly increasing tram travel times.

Previous research has shown that vehicles making U-turns at a signal-controlled urban road junction can negatively affect the performance of the junction. This paper reports on a study carried out to determine a suitable position to allow U-turns at signalised intersections in Tehran, Iran, based on their effects on network travel time. A section of the traffic network of Tehran was modelled using specialist software. To make the results more realistic, local traffic and driver behavioural parameters were calibrated and fed into the software. Various scenarios with different intervals were developed, with ten median openings before a signal-controlled junction and ten median openings after the junction. The results indicate that if the median opening for conducting U-turns is located 190 m before the junction, the network delay is lower than the other scenarios considered.

The functioning of public transport as a primarily social phenomenon leads to the realization of a significant social effect. The more deployed and high-quality the level of transportations is, the larger the social effect is. This also elevates the culture of passenger service and the use of modern environmentally friendly means of transport. The social effect has a multilateral manifestation, but the most significant one is its manifestation in the following trends: satisfying the population's various travel needs; reducing travel time and increasing free time, thus increasing the time for rest, relaxation, entertainment, educational and sport activities. The social effect is also achieved through the use of modern and environmentally friendly means of transport with reduced harmful emissions and noise impact on the population's health. The primary goal of socially effective public transport is to be accessible to everyone, to provide comfort, awareness, safety and security during passenger travel by incorporating efficient and stable urban intelligent transport systems and modern integrated environmentally friendly public transport with better emission indicators. The present report is dedicated to the main trends of increasing the social effectiveness of public transport in cities.

In this article, a bi-level transportation network design problem with environmental considerations is investigated. To explicitly reflect various requirements of environmental sustainability from planners, total emissions costs and total excessive noise cost are minimized along with total system travel time while performing optimal capacity expansion. To leave space for additional information on decision making for planners and provide a more generalized description of solution optimality, the Pareto optimization approach is adopted. The study proposes a multiobjective variant of a new metaheuristic named Chemical Reaction Optimization as the optimization tool to solve the formulated network design problem. Pareto front approximations have been successfully acquired for each scenario tested using the Sioux Falls network. The results show that the relations between pairwise objectives can change significantly (e.g., from conflicting to aligned) with regard to different demand situations. The newly proposed multiobjective metaheuristic succeeds to produce better approximations of the Pareto front than NSGA-II with comparable runtime for the considered road network.

The increasing number of vehicles and drivers have led to a marked increase in travel time expenditure (TTE), congestion, demand for fossil fuels, and adverse environmental impacts. Improving energy efficiency in the transportation sector, public awareness of the behavior of the people vis-a-vis energy efficiency, implementing policies that encourage other modes of transportation (e.g., public transit, ride-sharing, bicycles, and walking, etc.) that decrease vehicle dependency are some effective approaches that mitigate the aforementioned negative effects, which will lead to significant reductions in the total energy consumption. This article investigates the effect of governmental policies on vehicle dependency reduction and the decrease of TTE by vehicle owners, and propose a novel method to calculate the current and future TTEs by individuals. The effect of demographic variables and the region on vehicle dependency and TTE for students of three of the most populated universities in Malaysia (University of Malaya, University Putra Malaysia, and University Technology Malaysia) were investigated as well. The peoples’ expectations from individual modes of transportation such as cycling and walking were also analyzed. The results showed that all demographic factors, except nationality, affect the levels of vehicle ownership, while income levels and nationality affects TTE by personal vehicles. The results show that the average TTE can be reduced by 89% if the recommended infrastructure (e.g., increase bus routes, train routes, train services, frequencies of buses and train, and facilities for cyclists, etc.) is provided. These outcomes can assist policy makers to efficiently manage transportation budgets, and would also help people decrease vehicle usage, which will subsequently decrease their corresponding TTE and fuel consumption.

This paper presents a study on physical activity implications of electric bikes, focusing on the users of an on-campus conventional bicycle and e-bike sharing system at the University of Tennessee. The study describes field trials of 17 users of the bikesharing system and investigates physical activity metrics on identical trips made by three different modes: walk, conventional bicycle, and pedal-assist electric bicycle. The users completed a hilly 4.43 kilometer route using each mode. Heart rate and human power output were monitored along with GPS for each bout. In addition, the study used a laboratory test to relate oxygen consumption rate (VO 2 in ml/kg/min) and energy expenditure (EE kcal/kg/min) to user heart rate during bouts. Energy expenditure and ventilation rates (per minute) for all modes were not statistically different. However, total EE and VO 2 for each bout (per mile) for e-bikes are 24% lower than that for conventional bicycles, and 64% lower than for walking. This reflects the shorter travel time. Differences between e-bikes and bicycles are most pronounced on the uphill segment. Still, e-bikes provide moderate physical activity (MET>3) on flat segments and downhill segments, and vigorous physical activity (MET>6) on uphill segments. For e-bike trials, riders reported higher levels of enjoyment and lower need for a shower than walk or conventional bicycle trials. This paper adds to the expanding literature by comparing e-bike, bicycle and walk EE and VO 2 . E-bikes can contribute as an active transportation mode to meet required physical activity guidelines.

To estimate evacuation capacity of subway stations under emergency conditions, a bilevel programming model of evacuee equilibrium is built by considering travel time on walking facilities with vari...

We compared the performance of a Bluetooth beacon system and a reverse Radio-Frequency Identification (RFID) system to investigate their potentials as tools for measuring truck travel time in open-pit mines. A university campus was chosen as the simulation experiment site, and Bluetooth beacons and RFID tags were installed at intervals along the car’s course. A car equipped with a smartphone that can recognize beacons and an RFID reader that can communicate with the tags was driven along the course; the travel time and recognition rate were measured. For comparison of the travel time measurement data, we used video data captured while driving the car. The analysis results of ten rounds of experimental measurement data show that the average time error is 1.14 and 0.80 s for the Bluetooth beacon and reverse RFID systems, respectively. The sensor recognition rate is an average of 98% for the Bluetooth beacon system and 61% for the reverse RFID system. The reverse RFID system is slightly more accurate...

Predicting bus arrival times and travel times are crucial elements to make the public transport more attractive and reliable. The present study explores the use of Intelligent Transportation Systems (ITS) to make public transportation systems more attractive by providing timely and accurate travel time information of transit vehicles. However, for such systems to be successful, the prediction should be accurate, which ultimately depends on the prediction method as well as the input data used. In the present study, to identify significant inputs, a data mining technique, namely k-NN classifying algorithm is used. It is based on the similarity in pattern between the input and historic data. These identified inputs are then used for predicting the travel time using a model-based recursive estimation scheme, based on Kalman filtering. The performance is evaluated and compared with methods based on static inputs, to highlight the improved prediction accuracy.

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency ...

Few previous works integrated both uncertainty and environment disruption into traffic network design problems (NDPs). This study aims to address this gap. First, the mathematical framework of the strategic user equilibrium (StrUE) traffic assignment under volatility of both total travel demand and link capacity is analyzed. Second, we incorporate the StrUE traffic assignment into a network design project and propose a multi-objective bi-level NDP program. Two objective functions are formulated, which are respectively to minimize the expected total system travel time and minimize the expected total system off-gas emissions under StrUE. Third, we develop two tailored solution methods – an enumerative algorithm and a metaheuristic method based on a genetic algorithm. Finally, systematic evaluation of the performance of the proposed approach is conducted. The results highlight that ignoring uncertainty considerations can result in sub-optimal design solutions in terms of expected network performance....

Results in astrogeologic investigations are rarely communicated towards the general public by maps despite the new advances in planetary spatial informatics and new spatial datasets in high resolution and more complete coverage. Planetary maps are typically produced by astrogeologists for other professionals, and not by cartographers for the general public. We report on an application designed for students, which uses cartography as framework to aid the virtual exploration of other planets and moons, using the concepts of size comparison and travel time calculation. We also describe educational activities that build on geographic knowledge and expand it to planetary surfaces.