Intercity Highway Research Articles

Identifying and minimizing critical driving range thresholds for electric vehicles in intercity networks

It is well known that range anxiety faced by electric vehicle (EV) drivers nowadays can be effectively mitigated by either increasing the driving range of vehicles or increasing the distribution density of charging stations. To enact an effective research and development plan, vehicle manufacturers should understand well the relationship between the EV adoption and usage rates and the performance level of battery and energy management technologies. On the other hand, the underlying relationship between EV adoption and usage rates and the number and distribution of charging stations is also critical for charging infrastructure investors in determining their construction plans. This paper is dedicated to analytically identifying two critical driving range thresholds and further reducing these driving range thresholds as much as possible by optimally deploying charging station locations in intercity highway networks. These critical thresholds represent the turning points of individual routing convenience conditions or satisfaction levels for long-distance trips, implying that the inconvenient travel restrictions may be largely overcome if the driving range of EVs goes beyond these thresholds. For this purpose, we develop two integer programming models and dynamic programming algorithms for the driving range threshold identification problems, and extend the modeling framework to accommodate two relevant driving range threshold minimization problems as well, which are solved by a branching algorithm encapsulating the aforementioned dynamic programming procedures. The output of this research offers a set of new analytical tools for EV manufacturers to make strategic decisions on extending the driving range of their products and for charging infrastructure investors to make planning decisions on selecting charging station locations.

Optimal en-route charging station locations for electric vehicles with heterogeneous range anxiety

This paper addresses a new optimal charging station location problem for intercity highway networks where electric vehicles are of heterogeneous driving ranges and usually need to be charged multiple times in their long-haul trips. Driving range heterogeneity can be measured in practice by sampling the driving population under a variety of physical, environmental and psychological conditions and is characterized in this study by a continuous distribution of some appropriate forms. The behavioral assumption underlying this problem is that all individual drivers choose their optimal route-and-charge choices if they can make a trip by driving an electric vehicle or cancel their trips or switch to other transportation modes if they cannot. By explicitly taking into account the impact of limited driving ranges on both route and trip/mode choices, we constructed a mixed integer linear programming model for formulating the charging station location problem, the goal of which is to maximize the networkwide travel efficiency and preference subject to a limited infrastructure investment budget. To tackle this integer programming model, we developed a branch-and-bound algorithm and a neighborhood search heuristic, in both of which a multi-criterion label-correcting algorithm is embedded for deriving the underlying route-and-charge flow pattern. For justifying the effectiveness and efficiency of the proposed algorithms, a synthetic network and two real-world networks are employed as numerical examples. The computational results obtained from the numerical analysis show that the heuristic is capable of obtaining optimal solutions in most test scenarios and is much more computationally efficient than the branch-and-bound algorithm, with a computing time that is only about one tenth of the latter.

Towards Safer Bus Transport in Developing Countries: Geospatial Analysis of Bus Crashes on an Intercity Highway in Ghana

Towards Safer Bus Transport in Developing Countries: Geospatial Analysis of Bus Crashes on an Intercity Highway in Ghana

Optimal en-route charging station locations for electric vehicles: A new modeling perspective and a comparative evaluation of network-based and metanetwork-based approaches

In many regions across the world, the distribution density of electricity-charging stations is yet sufficient for taming the range anxiety concern of electric vehicle drivers. In such driving circumstances, making detours to find alternative charging opportunities is often an operational remedy to accomplish long-distance trips. To reduce the frequency and extra mileage of detouring, public charging infrastructures should be coordinately sited and constructed in a regional network. With the goal of minimizing the possible detour mileage, this paper describes an optimal charging station location problem for intercity highway networks and develops two approaches for modeling and solving it. Specifically, two mixed linear integer programming models are constructed in different network modeling paradigms: The first one is formed and solved on the basis of the original node-link network topology, with newly introduced integer variables such as path and subpath activation indicators to quantify the utilization status of paths and subpaths, respectively. The classic branch-and-bound algorithm encapsulating a bi-criteria label-correcting algorithm is designed for solving this network-based model. The other one is formulated and solved relying on the station-subpath metanetwork topology, as the result of a two-phase process that decomposes an individual routing and charging decision into two parts. The two phases are conducted on the node-link network and station-subpath metanetwork levels, respectively, where the first phase is done by solving a network-based, distance-constrained minimum-cost path problem, while the second phase collapses to a metanetwork-based, simple minimum-cost path problem. The findings we obtained through conducting this research are twofold: First, analytical and computational studies clearly identify the effectiveness of the two modeling and solution approaches, while the metanetwork-based approach exhibits its appealing computing advantage for solving problems of large size; second, the application of the solution approaches for a real-world network instance reveals how budget limit and distance limit impact the charging station locations, individual routing and charging decisions, and network cost and accessibility levels.

Daily electricity demand assessment on the example of the Turkish road transport system – a case study of the development of electromobility on highways

The aim of this study is to investigate how the daily electricity demand from road transport related to the implementation of an electric road system on the eight roads with the highest traffic flow connecting the seven largest cities in Turkey varies according to time and location. Intercity highway route O-7, O-5, O-21, D715, D687, E96, and E87 in Western Turkey was used as a case study. The daily electricity demand on the eight roads working on the full electrification of the existing traffic flow can be increased by 3.7% in the case of the reference point. However, if all roads in Turkey are converted to an electric road system and all land vehicles use this system, the corresponding peak power increase will be 100%. The daily electricity demand along the roads is derived from the available measuring points for the daily road traffic volumes. The study also compares the CO₂ reduction potentials and energy demands of the electrified road system with the use of fossil fuels to achieve the same transportation volume. The results show that an electric road system application on eight Turkish roads with considerable traffic flow can reduce 18.8 million tons of CO₂ emissions from the road transport sector. The research can find practical application in assessing the validity of developing a strategy for the development of electromobility on highways in Turkey.

Analysis of Changes in Intercity Highway Traffic Travel Patterns under the Impact of COVID-19

The unprecedented COVID-19 pandemic impacts negatively on the security and development of human society. Comparison and analysis of intercity highway travel patterns before and during the COVID-19 pandemic can bring vital insights for the prevention and control of the pandemic. Empirical studies are conducted using cellular network-based datasets associated with two groups of city pairs in China heavily affected by COVID-19. Spatial matching, full-sample extrapolation, and trajectory feature analysis are adopted to attain travel volumes of intercity highways during four different periods. The reliability of origin-destination (OD) matrices calculated based on the cellular network-based dataset is demonstrated by comparing with the fluctuation trend of traffic count data. The empirical studies show that the OD flows associated with passenger cars on intercity highways in China decreased significantly during COVID-19. With the effective implementation of the pandemic prevention control policy and the orderly promotion of the recovery to work and production, the volumes of intercity highway OD flows returned to the pre-pandemic level in mid-April 2020. Besides, the peak of passenger car trips decreases and the time span for truck trips gets longer owing to implemented control measures in dealing with COVID-19. The results can be applied to the calculation of OD flows between most adjacent cities and analyze the intercity highway traffic travel patterns changes, which provide insightful implications for making intercity travel safety prevention and control policies under epidemic conditions.

Ambient Air Pollutions along a Major South-Western Nigerian Road

This study investigated the air pollutants in the ambient air of a typical intercity highway in Nigeria. This was to assess the effect of vehicular emission on air quality along the highway. The results showed that NOX concentrations ranged from 9.9±3.2 to 33.8±3.3 µg/m3 during the wet season and 19.0±1.2 to 35.4±2.3 µg/m3 during the dry season. Sulfur dioxide measured along this highway ranged from 49.7±38.1 to 219±18.1 µg/m3 during the wet season while dry season concentration ranged from 89.1±20.9 to 225.4±57.9 µg/m3. The TSP during the wet season ranged from 54.4±25.6 to 126.8±25.6 µg/m3. These values were below the limits of 250 mg/m3 set by FMEnv., and 150-230 mg/m3 by WHO. However, the TSP measured during the dry season ranged from 85.9±44.6 to 277.8±213.5 µg/m3. The average correlations between NOX, SO2, and TSP measured during wet and dry seasons and the traffic density were 0.7, 0.6 and 0.7, respectively. Air pollution along the Nigerian highway is highly linked to vehicular activities.

내비게이션 자료를 활용한 중장거리 경로 선택 행태 분석

내비게이션 자료를 활용한 중장거리 경로 선택 행태 분석

Optimization of vehicle speed forecasting horizont on the intercity highway

The movement of the car in the traffic on intercity routes was investigated. Traffic should be energy efficient, safe and comply with the desired schedule. A method for analyzing the input data flow based on a simulation model has been developed. The proposed simulation algorithm is based on the use of available information resources for driving a car. Traffic control involves choosing a speed with known road and traffic restrictions. The presented algorithm allows to consider the expediency of each of speed increase opportunities over the forecast horizon. The content of the algorithm is the optimal redistribution of time resources. Indicators of control quality are absolute deviations from the optimal energy-saving program of free movement and from the planned schedule. The movement of a freight road train on the long-distance highway E−371 was performed. It was found that the total amount of information increases with increasing distance of scanned traffic. However, the share of reliable information is reduced. It was found that the dependence of the quality of vehicle traffic control on the size of the forecast horizon is piecewise-continuous. The dependence has an extreme value of the horizon in each continuous section, at which the deviation from the optimal program is minimal. The obtained results can be applied in modern intelligent transport systems. The research results make it possible to develop and adhere to optimal long-term traffic programs on highways. It solves the problem of managing large data streams. Large amounts of information for forecasting can be submitted in parts with reasonable frequency using the developed methodology

A Novel Approach to Estimating Missing Pairs of On/Off Ramp Flows

A freeway stretch with even one pair of unmeasured on/off-ramps is not fully observable in traffic states. Flow observability is essential for freeway traffic modeling, surveillance, and control. It is a longstanding and tricky issue to estimate flows for unmeasured ramp pairs. This problem seems to be hardly tractable in conventional approaches, and this paper intends to handle it based on machine learning. The work was partially inspired by transfer learning. Consider that no measurements are available for a target ramp pair, and the knowledge about ramp flow estimation may be drawn from other (measured) ramp pairs, provided that measured and unmeasured ramp pairs would share similarities in some key traffic flow patterns. Two simple machine learning algorithms, random forest (RF) and gradient boosting machine (GBM), were employed to this end. RF and GBM were driven by real measurement data to establish models that relate ramp flows to adjacent mainstream traffic conditions. The models were then applied for our task. The estimation performance was evaluated using real measurement data from the Shanghai Urban Expressway and the Intercity Highway in California, with satisfactory results obtained.

An ensemble-learning method for potential traffic hotspots detection on heterogeneous spatio-temporal data in highway domain

Inter-city highway plays an important role in modern urban life and generates sensory data with spatio-temporal characteristics. Its current situation and future trends are valuable for vehicles guidance and transportation security management. As a domain routine analysis, daily detection of traffic hotspots faces challenges in efficiency and precision, because huge data deteriorates processing latency and many correlative factors cannot be fully considered. In this paper, an ensemble-learning based method for potential traffic hotspots detection is proposed. Considering time, space, meteorology, and calendar conditions, daily traffic volume is modeled on heterogeneous data, and trends predictive error can be reduced through gradient boosting regression technology. Using real-world data from one Chinese provincial highway, extensive experiments and case studies show our methods with second-level executive latency with a distinct improvement in predictive precision.

Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China

Abstract Emissions from heavy-duty diesel trucks (HDDTs) pose a major threat to environment and human health. Understanding the behavior of pollutant emissions from HDDTs facilitate the formulation of traffic-related policy measures to mitigate the adverse effects. This study proposes a new method to estimate the emission inventory of HDDTs and analyze their spatiotemporal evolution characteristics. Multi-source data were fused to provide a complete picture of the transport environment. With the idea of modeling pollutant emissions by “single vehicle” and “road segment,” emission inventories were constructed with different spatiotemporal scales using localized emission factors. A spatiotemporal cube model was introduced to represent the high-resolution emission inventory. A hot-spot and local-outlier analysis were conducted to explore the spatiotemporal evolution mechanism of pollutant emissions. The megacity of Tianjin in China was taken as the area for the case study. The average daily emissions of CO, NOx, PM, and VOC are 12,978.18, 48,675.22, 712.6, and 1217.72 kg d−1, respectively. Temporally, the pollutant emission had a significant peak at 06:00, 11:00, and 18:00 and was affected by major festivals. Spatially, the distribution pattern of emission was policy-driven and closely related to its spatial location. It increases radially outward from the outer-ring road to the periphery. The hot-spot analysis identified 16 pollutant emission patterns. The road segment with persistent cold spots accounted for 48.27% of the roads, mainly distributed within the outer ring road. The road segments with intensifying and persistent hot spots accounted for 19.04% of roads, mainly distributed on the intercity highway. The locations with outlier values reached 12,027, accounting for 31.90%. The key time intervals of the occurrence of the outlier pattern are 11:00–12:00 and 01:00–02:00. Road segments showing only the low–low cluster pattern is mainly located within the outer ring roads. Those with only low–high outlier pattern exhibits a relatively scattered spatial distribution, which is affected by the heterogeneous distribution of HDDTs.

Development of vehicle speed forecasting method for intelligent highway transport system

Interaction of vehicles on an intercity highway is considered. The vehicle control model here is idealized, close to the 4th generation automated intelligent transport system. Each vehicle has the desired motion program, independent of the driver's motives, which is justified by minimum resource consumption and compliance with the desired schedule. The diversity of programs affects their unwanted change. The aim was to identify the dependence of the actual vehicle speed on traffic flow parameters. The main task was to reveal a direct parameter for changing the motion program. The use of simulation models based on cellular automata is substantiated. A new cellular automaton, which is a sliding window with a reference point, which is the observer vehicle is developed. The number of objects in the field increases periodically and is constant. All cells on the left and right of the reference point of the automaton form the information field, or the total length of the automaton. The automaton height, which depends on the type of highway, is modeled. The rules of objects movement in the automaton grid at each iteration are finite, established and similar to the Schreckenberg automaton, except for randomization, which is minimized in this model. Such an automaton reflects relative speeds of vehicles relative to the observer vehicle, as well as the ability to reproduce accelerations. At each iteration, the change in vehicle speeds is calculated. The simulation algorithm is programmed in the Delphi language. Simulation of the vehicle movement on the E-471 international highway is performed. On the 20 km section of this route, traffic flows with different density and speed distribution are modeled. The quadratic correlation dependences of the forced change in the desired speed of the observer vehicle on changes in the average speed of the flow vehicles are revealed. The degree of agreement between the theoretical dependence and empirical data is very high. On the basis of the dependencies obtained, the choice of the direct diagnostic parameter of the traffic flow is justified.

Travel Time Reliability as a Level of Service Measure for Urban and Inter-Urban Corridors in India

The present study demonstrates the application and usefulness of travel time reliability as a level of service (LOS) measure for urban arterial and inter-urban highway corridors on Indian roads. For travel time estimation, automatic vehicle license plate number data were collected through TrafficMon system. This system is a fully video-based enforcement system designed to measure the speed of vehicles passing in view of the camera, and read the vehicular license plate number. This system was implemented at the entry and exit side of the identified three study corridors and data were collected during morning and evening peak periods. The data were analysed and various travel time reliability measures were evaluated. The study also attempts to correlate reliability measures such as planning time (PT), buffer time (BT), planning time index (PTI) and buffer time index (BTI) with volume-to-capacity ratio which is the most widely used LOS measure for Indian roads. Analysis of results indicated that at LOS B the travel time of intercity highway was 40–46 sec/km, whereas it was 64–80 sec/km and 75–135 sec/km for urban uninterrupted and interrupted corridors respectively. The planning time for LOS B was more on urban arterial corridors when compared to inter-urban corridor for the same width of the carriageway. The upper limits of LOS B for PT were 132 sec/km and 63 sec/km for uninterrupted urban corridor and intercity highway corridor respectively. Other parameters of reliability like PTI and BTI were also evaluated and their values for different ranges of volume-capacity ratio were presented for identified corridors.

Empirical study of lane-changing behavior on three Chinese freeways.

Lane-changing (LC) behavior is investigated on Chinese freeways, where the driving circumstances are relatively aggressive. Three data sets were collected from urban expressways and an intercity highway in the form of traffic videos. Different aspects of LC behaviors are analyzed, i.e., the LC rate, motivation, target lane choice and impact on traffic. The results suggest that LC is a transient behavior that randomly occurs with high frequency, which is the main feature of aggressive driving. Several LC patterns and the combination effect of ramps, fast lanes and various vehicle types are presented. The influence of LC on local traffic endures for approximately 15 to 30 s, which rapidly increases and slowly declines. LC behavior will increase the risk of high-speed car-following. All results are obtained from the empirical data; they will be useful for traffic management and traffic modeling.

A Model of Calculating the Charging Demands of Electric Vehicles in Running Based on Large Sample Size of Highway Transportation Data

To satisfy the travelling demands of large scale of electric vehicle (EV) in intercity highway, a model of calculating the charging demands of EVs running in highway is built. In this model, the driving speed, maximum driving distance, the remaining capacity of batteries, geographic information of road sections and charging stations (CS), spot price and the capacity of chargers for EVs running in highway are all considered. By introducing charging willing of users, a queuing algorithm is used to solve the model built and the waiting time of each CS in the road sections that EVs plan to run. Experimental results show the model is adopted to design a reasonable charging plan according to users’ willing.

Integration of Express·Intercity Bus Terminal and Highway Service Area: A Study of the Estimated Tangible Value Addition

Integration of Express·Intercity Bus Terminal and Highway Service Area: A Study of the Estimated Tangible Value Addition

Influence of geometric design characteristics on safety under heterogeneous traffic flow

Abstract This paper focuses on analysing the influence of geometric design characteristics on traffic safety using bi-directional data on a divided roadway operated under heterogeneous traffic conditions in India. The study was carried out on a four lane divided inter-city highway in plain and rolling terrain. Statistical modelling approach by Poisson regression and Negative binomial regression were used to assess the safety performance as occurrence of crashes are random events and to identify the influence of the geometric design variables on the crash frequency. Negative binomial regression model was found to be more suitable to identify the variables contributing to road crashes. The study enabled better understanding of the factors related to road geometrics that influence road crash frequency. The study also established that operating speed has a significant contribution to the total number of crashes. Negative binomial models are found to be appropriate to predict road crashes on divided roadways under heterogeneous traffic conditions.

Investigation of site properties in Adapazarı, Turkey, using microtremors and surface waves

Determination of suitable areas for new residential buildings is crucial to increase the resilience of buildings against earthquake hazards. After the 1999 Izmit earthquake Mw 7.4, the city of Sakarya has been expanding rapidly in terms of both the population and number of superstructures. Despite the fact that Sakarya suffered several large earthquakes in the last two decades, the geophysical properties of the region have not been adequately investigated. In this study, the site properties of Sakarya University, Esentepe Campus, and its surrounding Serdivan district, which is one of the important parts of the city, are determined using microtremor measurements and surface wave analysis. Nakamura’s spectral ratio method (spectral ratio between the horizontal and vertical components, H/V or HVSR) was used to determine the fundamental frequency and site amplification values. The shear wave velocity profiles of the studied sites were determined using the multichannel analysis of surface waves method. Seismic measurements were performed at 34 locations to record the surface waves in the area. The fundamental frequency and site amplification values are determined as 1.02–11.68 and 1.33–5.96 Hz, respectively, from the microtremor measurements. The fundamental frequency is between 4.0 and 11.5 Hz in the campus area and between 1.3 and 2.0 Hz near the D-100 intercity highway. The site amplification was determined to be 1–2.5 in the campus area. The greatest site amplification (6) is obtained at thick alluvium deposits in the valley between hills. The average shear wave velocity values are within the range of 300 and 1120 m/s. Some parts located on the hill area have better soil condition (B) categories, according to the National Earthquake Hazard Reduction Program, and have comparatively high shear wave velocities in the range of 740–1080 m/s, whereas low velocity values are found at the thick alluvium deposits (D). We found that the geological properties and topographies play very important roles on the shear wave velocity and the amplification factors in the investigated area.

한국도시의 형태변화 모델

This study aims to develop a model that would explain the morphological transformation and development pattern of the Korean cities. To do so, it proposes HanJoo, a hypothetical city that would experimentally characterize the development process of formation, growth and decline of small and medium-sized cities. HanJoo focuses particularly on functional relationship with the adjacent cities in regional setting, locational orientation and topographical condition of the city site, and morphological characteristics and structural shift of the city growth characterized by a set of four-staged development phases: (1) walled town (675-1890); (2) town expansion via modern grid blocks (1890-1945); (3) industrial district and the company town linked by inter-city highway (1946-1994); (4) new community development and massive suburban growth (1995-present). The findings expect to be useful for improving the understanding the Korean cities particularly whose city layout and locational features in the formative stage, their transformation in the Japanese colonial period, growth and decline mechanism in modern and contemporary settings have not theoretically identified.