Intercity Trips Research Articles

When green transportation backfires: High-speed rail's impact on transport-sector carbon emissions from 315 Chinese cities

High-speed rail is often viewed as a green transportation mode, substituting for carbon-intensive vehicular and aviation trips. However, its potential to induce additional travel demand and transport-sector carbon emissions has been largely overlooked. Here, we empirically examine the impacts of high-speed rail accessibility, the ease with which one city can reach other cities via the high-speed rail network, on transport-sector carbon emissions across 315 Chinese cities between 2010 and 2020, using a two-stage least squares model. Contrary to the anticipated emission-reduction effect, our analysis suggests a net positive impact of high-speed rail on transport-sector carbon emissions. Specifically, a 1% increase in high-speed rail accessibility leads to a 0.18% increase in transport-sector carbon emissions in the long run, as carbon emissions generated from induced travel demand have outweighed the carbon savings from substitution for carbon-intensive inter-city trips. This high-speed rail-induced increase in carbon emissions is observed in the road transport subsector, while the aviation subsector exhibits a compensatory reduction. Moreover, we find that integrating the high-speed rail network into local subway systems can curb the emission-intensifying effect, highlighting the importance of joint public transit planning. This study reveals an unexpected environmental impact of high-speed rail, conveying valuable insights into transport-sector decarbonization.

Emission Control in Expressway Systems: Vehicle Emission Inventory and Policy Scenario Analysis

Expressway systems play a vital role in facilitating intercity travels for both passengers and freights, which are also a significant source of vehicle emissions within the transportation sector. This study investigates vehicle emissions from expressway systems using the COPERT model to develop multi-year emission inventories for different pollutants, covering the past and future trends from 2005 to 2030. Thereinto, an integrated SARIMA-SVR method is designed to portray the temporal variation of vehicle population, and the possible future trends of expressway vehicle emissions are predicted through policy scenario analysis. The Jiang–Zhe–Hu Region of China is taken as the case study to analyze emission control in expressway systems. The results indicate that (1) carbon monoxide (CO) and volatile organic compounds (VOCs) present a general upward trend primarily originating from passenger vehicles, while nitrogen oxides (NOx) and inhalable particles (PM) display a slowing upward trend with fluctuations mainly sourcing from freight vehicles; (2) vehicle population constraint is an effective emission control policy, but upgrading the medium- and long-haul transportation structure is necessary to meet the continuous growth of intercity trips. Expressway vehicle emission reduction effectiveness can be further enhanced by curtailing the update frequency of emission standards, along with the scrapping of high-emission vehicles.

Regional Truck Travel Characteristics Analysis and Freight Volume Estimation: Support for the Sustainable Development of Freight

In the field of freight transport, the goal of sustainable development requires us to improve the efficiency of freight transport while reducing its negative impact on the environment, such as reducing carbon emissions and noise pollution. There is no doubt that changes in freight characteristics and volumes are compatible with the objectives of sustainable development. Thus, mining the travel distribution and freight volume of trucks has an important supporting role in the freight transport industry. In terms of truck travel, most of the traditional approaches are based on the subjective definition of parameters from the trajectory data to obtain trips for certain vehicle types. As for freight volume, it is mostly estimated through manual surveys, which are heavy and inaccurate. In this study, a data-driven approach is adopted to obtain trips from the trajectory data of heavy trucks. Combined with the traffic percentage of different vehicle types collected by highway traffic survey stations, the trips of heavy trucks are extended to all trucks. The inter-city and intra-city freight volumes are estimated based on the average truck loads collected at the motorway entrance. The results show a higher proportion of intra-city trips by trucks in port cities and a higher proportion of inter-city trips by trucks in inland cities. Truck loading and unloading times are focused in the early morning or at night, and freight demand in Shandong Province is more concentrated in the south. These results would provide strong support for optimizing freight structures, improving transportation efficiency, and reducing transportation costs.

Meeting the charging demand of Electric Vehicles in Greece: Enabling intercity trips

The increasing need for sustainable transportation has underscored the pivotal role of electric mobility in shaping the future of mobility. Electric Vehicles (EVs) have emerged as a promising solution, but their widespread adoption is contingent upon the availability of a reliable charging network. This challenge extends even to EV adoption pioneers like Norway and China. This study delves into the context of EV adoption in Greece, providing insights into the EV market, existing incentives, and the state of charging infrastructure. Reports indicate that Greece is at a critical juncture, with fast EV adoption but sluggish development of Charging Stations (CSs). To address this issue, a Monte Carlo simulation is employed to evaluate the feasibility of long-distance EV trips in Greece and propose an infrastructure development plan. For EVs, the study reveals a linear relationship between battery size and autonomy, with some exceptions emphasizing the importance of data acquisition and individual EV assessment. The study also provides insights into the relationship between trip length and energy consumption, indicating that longer trips exhibit greater fluctuation in energy consumption due to varying conditions, while energy usage patterns of shorter trips are more predictable. Additionally, the analysis presents average energy consumption values for each EV model in different temperature conditions, highlighting the impact of environmental factors on EV performance.

Spatiotemporal characteristics of residents' intercity travel in China under the impact of COVID-19 pandemic

Investigating the spatiotemporal characteristics of residents' intercity travel affected by the coronavirus disease (COVID-19) pandemic is of great significance to understanding and extending the impact of major public health affairs on intercity travelers, urban management policies, and intercity cooperative interactions. Taking 366 cities in China as an example, this study proposed a novel research framework for analyzing the spatiotemporal characteristics of intercity population flows. Using AutoNavi population migration big data sets from web crawling, the spatiotemporal structure patterns of intercity travel in China from January 1st to June 30th, 2020 were extracted by using the singular value decomposition (SVD) method. Then the spatial organization patterns of intercity travel networks in China and its five major urban agglomerations were identified by the weighted stochastic block model (WSBM). The results showed that the total volume of intercity travel in China and its five major urban agglomerations experienced a process of rapid decline followed by a slow recovery, presenting a “V-shaped” distribution since the outbreak of the COVID-19 pandemic. Three distinctive intercity travel patterns, namely the stable, holiday, and weekday-weekend intercity trip patterns, were extracted from the spatiotemporal OD matrix of intercity travel. Strong intercity travels were mainly distributed in eastern and central China, forming an intercity travel pattern in which core cities pushed and pulled each other within the national urban agglomerations. However, intercity migration flows were dominated by weak-intensity flows. Intercity trips during the holidays had a phenomenon of “escaping from the metropolis”. Meanwhile, it had significant spatial proximity and directionality. The daily intercity trips were characterized by a remarkable periodicity with seven days as a repeat. A more interesting finding is that when weekends overlap with holidays, intercity travel on the 1–2 days before the vacations has presented an effect of “suppression travel”. Furthermore, at the national scale, the stable intercity travel network has formed a polycentric core-periphery structure of “Three Cores-Six Semi-peripheries-Five Peripheries”. At the urban agglomeration scale, they formed respectively distinguished monocentric, bicentric, and polycentric core-periphery structures.

Polycentricity measurement of China’s urban agglomerations considering internal and external connections

This paper uses Baidu migration data to obtain the intercity trips between cities in China. Taking intercity travel as a functional connection between cities, taking into account the internal and external intercity travel connections of urban agglomerations, an improved rank scale distribution method was used to quantitatively measure the form and function of the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, Chengdu-Chongqing, Central Plains, Guanzhong, and Harbin-Changchun urban agglomerations. It is found that, firstly, there are differences in the Functional Polycentricity presented by the internal and external connections of urban agglomerations, which are related to the economic development of urban agglomerations and central cities. Secondly, there are differences between Morphological Polycentricity and Functional Polycentricity of the eight urban agglomerations measured by the internal and external connections, indicating that the external connections have a significant impact. The internal and external connections of urban agglomerations are important functional connections of urban agglomerations. The study of urban agglomerations should not ignore the internal or external connections, and the overall consideration can more comprehensively reflect the polycentric characteristics of urban agglomerations. The internal and external connections of urban agglomerations are important functional connections, and the study of urban agglomerations should not ignore internal or external connections. Overall consideration can better reflect the polycentirccharacteristics of urban agglomerations.

A service network design for scheduled advanced air mobility using human-driven and autonomous air metro

Emerging modes of advanced air mobility are potential alternatives to current ground transport. This study proposes a service network design approach for the air metro, a pre-scheduled service with fixed routes that accommodate passengers for intra- or inter-city trips. The scenarios of human-driven and autonomous air metro are then compared, where the former has a labour cost for pilots and the latter has a higher capital costs such as vehicle and automation costs. Then, a rolling horizon optimisation approach is proposed, where the temporal length of a single rolling horizon is an early confirmation period plus a safety margin. The rolling horizon introduces decision and marginal arcs with different fleet, passenger, and pilot network capabilities. The optimised outputs on critical arcs are determined and fixed, while the marginal arcs can be continuously adjusted in the subsequent rolling horizons. Numerical studies are undertaken across all variables in the context of the Greater Metropolitan Area of Sydney, Australia. Results suggest that the human-driven air metro would be economically preferable until the utilisation cost of an autonomous aircraft can reduce by 60%. Furthermore, confirming the actual passenger demand at least 45 min in advance is recommended, and a single rolling horizon should be longer than 150 min.

A study on the factors influencing the adoption of urban air mobility and the future demand: Using the stated preference survey for three UAM operational scenarios in South Korea

This study analyzed commuters’ intention to use urban air mobility (UAM) and the future demand for UAM based on its service characteristics and the personal characteristics of the potential users. The Seoul Metropolitan Area (SMA) was selected as the study area as it is expected to have the highest demand for UAM in South Korea. Three UAM operation scenarios were set: short-distance intra-city trips, long-distance intra-city trips, and inter-city trips. Five alternatives were assumed for the stated preference (SP) survey: bus, subway, taxi, private car, and UAM. Six alternative-specific variables were selected: access time, waiting time, boarding time, cost, autonomous mode, and the presence of other passengers. An online survey of 300 residents of SMA was conducted with 100 residents for each scenario. The residents were briefed on the modes of transportation and service characteristic of alternatives. The analysis results were as follows: (1) cost and access time significantly influenced the intention to use UAM in all scenarios. However, the effects of waiting time, boarding time, and autonomous driving on intention to use UAM differed based on the scenarios; (2) the individual characteristics of potential users had varying effects on their preference for UAM depending on the scenarios and the means of transportation, which was a counterpart to UAM. (3) The probability of choosing UAM for commuting was expected to be 10–20%, with the highest modal split for long-distance intra-city trips and the lowest modal split for short-distance intra-city trips. Among the six alternative-specific variables, autonomous driving and cost had the most significant effect on the change of the probability for using UAM. The results of this study are expected to be used as basic data for designing an effective strategy for the introduction of UAM in South Korea.

Stimulus Perception in Long-Distance Railway Mode Choice

In the context of countries in the so-called Global South, where passenger railway services are either nonexistent or poorly performed, discrete choice models are useful to identify the attributes that affect users’ choices and provide insights on their behaviour in regional long-distance trips. Several theories and models have been proposed to understand travel behaviour for effective strategical decision in the transport field. The well-knownRandom Utility Maximization (RUM) approach has been widely used for such purposes, while the Random Regret Minimization (RRM) approach has been recently explored in the literature. However, the magnitude in the difference of levels of the attributes, or the stimulus perception, may affect the results of such models and biases the estimations. Therefore, this paper aims to assess the stimulus perception in mode choice to compare conventional rail (CR) and high-speed rail (HSR) services for passenger transport in intercity trips in Brazil. Estimations of RUM and RRM models were performed with a dataset from a stated preference survey comparing two railway technologies (CR and HSR) with other modes of transport (car, bus, and airplanes) for long-distance trips in the Southeast region of Brazil. Findings provide useful insights about the impacts of travel costs, travel times, and frequency of services, as well as sociodemographic characteristics of users. From the modelling outputs, it was found that users are affected by the magnitude of travel costs, time, and frequency only in business trips by HSR in the Brazilian context.

Evaluation and improvement of the urban transportation networks resilience in short-term non-recurring traffic congestion: a novel graph connectivity-based criteria

Work or study purpose trips usually make up most intercity trips during peak hours. Being at a destination at a specific time is one of the characteristics of such trips, and the occurrence of non-recurring short-term traffic congestion can damage these trips. Due to the necessity of the subject, this study examines the urban transportation networks resilience (TNR) in the face of non-recurring traffic accidents with short-term effects. This paper defines a novel criteria to measure the urban TNR by developing the connectivity concept graph theory. Then, based on the proposed criteria, the resilience of the Isfahan transportation network (one of Iran's metropolises), has been evaluated, and the critical nodes and arcs of this network have been identified. Also, by performing sensitivity analysis, the role of the travel time satisfaction threshold is discussed. Finally, a series of projects have been introduced, which are believed to increase Isfahan's TNR. The projects have been compared and evaluated based on the proposed method.

A Methodology for the Estimation of Value-of-Time Using State-of-the-Art Econometric Models

Value-of-time (VOT) measures are valuable in a wide range of public transport policy and planning applications. However, VOT is a latent variable that cannot be measured directly. In this research, state-of-the-art econometric models are developed within a methodological framework that allows for the estimation of the VOT. Ordered and binary discrete choice models have been developed. Furthermore, a mixed effects model that accounts for the unobserved heterogeneity across different individuals has also been specified. The models have been applied to short intercity trips between two medium-size cities (Agrinio and Patras) in Greece. The model specification combines trip-based characteristics (mode, travel time, and travel cost), with socioeconomic characteristics, such as profession, education, and car ownership. A stated-preference survey has been designed and administered to a random sample of 289 people. The estimated coefficients from the developed models have been used to estimate VOT measures and the overall performance of the ordered logit and the generalized linear mixed model has been found to be superior to the binary logit model.

Environmental impacts of enlarging the market share of electric vehicles

We extend a multimodal transport model to simulate an increase of the market share of electric vehicles. The model, which is described in detail in Kilani et al. (Sustainability 14(3):1535, 2022), covers the north of France and includes both urban and intercity trips. It is a multi-agents simulation based on the MATsim framework and calibrated on observed traffic flows. We find that the emissions of pollutant gases decrease in comparable proportion to the market share of the electric vehicles. When only users with shorter trips switch to electric vehicles, the impact is limited and demand for charging stations is small since most users will charge by night at home. When the government is able to target users with longer trips, the impact can be higher by more than a factor of two. But, in this case, our model shows that it is important to increase the number of charging stations with an optimized deployment for their accessibility.

The effectiveness of fixed speed cameras on Iranian taxi drivers: An evaluation of the influential factors.

BackgroundThe adherence to speed limits can reduce deaths associated with road traffic injuries (RTIs) by more than a quarter. This study aimed to evaluate the effective factors on the speeding behavior of Iranian taxi drivers around fixed speed cameras.MethodTelematics devices used in this study collected the location and speed of the vehicles. The units of analysis in this study were trips per camera, including 2.5 km before and after each camera. Linear regression analysis was used to identify kangaroo driving (KD), defined as trips with a significant V-shape in speed distribution around the camera. In the clustered camera zones, cameras were placed at regular intervals of approximately 3.5 km.FindingsA total of 93,160 trips were recorded from 13,857,443 data points. There was an inverse association between drivers' age and KD with an odds ratio (OR) of 0.98 (95% confidence interval: 0.98–0.98). The intercity trips had a substantially higher probability of KD than urban trips (OR: 4.94 [4.73–5.16]). The tendency of drivers toward KD during the daylight hours vs. nighttime was significant for both urban (OR: 1.15 [1.06–1.25]) and intercity (OR: 1.18 [1.14–1.22]) trips. The 4 −8 a.m. period had the highest chance of KD in both urban (10.71% [7.41–14.53]) and intercity (44.13% [41.18–47.09]) trips. There was a significant decrease in the odds of KD inside the clustered camera zones (OR: 0.22 [0.20–0.25]).ConclusionThe heterogeneous occurrence of KD in different locations and times indicates the necessity of evidence-based decision-making in urban planning to improve safe driving behaviors. The lower occurrence of KD in clustered camera zones could be a practical key to the effective control of speeding behaviors by helping in the efficient placement of cameras through sustainable development of cities and roads.

Effects of electric vehicle adoption for state-wide intercity trips on emission saving and energy consumption

Electric vehicles (EVs) mitigate fossil fuel dependency and reduce emissions; they are considered sustainable alternatives to conventional vehicles. To support EVs’ intercity trips, a recent study proposed a charging infrastructure planning framework implemented for the intercity network of Michigan, considering different projected EV market shares for 2030. This study aims to estimate emission savings, including CO2, HC, CO, and NOx, associated with the projected electrification rate and the proposed infrastructure for light-duty vehicles in the earlier study. To this end, a state-of-the-art emission estimation framework is proposed for state-wide intercity trips. The main contributions of the proposed framework include (1) Incorporating a micro emission estimation model (MOVES) for the simulated vehicle trajectories in a large-scaled network considering monthly travel demand, temperature variations, and heterogeneity of vehicles; (2) Contrasting the required investment in charging infrastructure to support EVs intercity trips with the associated emission savings. To this end, the proposed emission estimation framework is benchmarked against a traditional method based on vehicle miles traveled (VMT). Then, different scenarios of EV adoption are explored to assess potential emission savings. The results suggest that the annual CO2 savings (as the most critical emission) due to intercity travel electrification ranges from 0.34 to 1.45 million ton. The societal cost savings justify the investment in network electrification. Note that only 3.8%–8.8% of the EV energy demand must be satisfied by the DC fast charger network proposed by the charging infrastructure planning framework. This requires 22.45–51.60 BWh annual energy consumption for Michigan’s EV market share for 2030.

System of Systems Model for Planning Electric Vehicle Charging Infrastructure in Intercity Transportation Networks Under Emission Consideration

Rapid development of electric vehicles holds the promise to significantly mitigate greenhouse gas emissions. However, the lack of pervasive en-route charging infrastructure on regional highway networks hinders the use of electric vehicles for long-distance intercity trips. Traditionally, transportation and power systems are often studied separately, each by itself as a complex system. This paper proposes a system of systems modeling framework for planning charging infrastructure deployment in intercity transportation networks while considering users’ travel behavior and coupling relationships between transportation and power systems. The aim is at facilitating long-distance electric vehicle travels and providing an effective and comprehensive tool to evaluate the total emissions from both the transportation and power sectors. We look at a somewhat “forward-looking” problem in which the charging loads of widely-adopted electric vehicles induce interdependence between the power and the transportation sectors. A general equilibrium modeling framework is developed to capture the interdependencies among charging infrastructure design, users’ travel behaviors, and power sector operations. An iterative solution approach is proposed to solve the overall equilibrium between the transportation and power sectors, and a heuristic algorithm is developed to solve the bi-level subproblem for the charging infrastructure deployment. Numerical experiments based on a semi-realistic case study are performed to demonstrate the applicability of the proposed modeling and solution approach.

Shifting the population mobility of the Ukraine western region on the strength of the COVID-19 pandemic

The Covid-19 pandemic has significantly affected the economic and social spheres of all countries. Restrictions intro-duced to reduce the risk of transmission have changed the structure of population movements. The impact of these restrictions on the characteristics of intercity travel is still an understudied problem. Based on the analysis of statisti-cal data and the results of questionnaires, the article assesses the impact of pandemic restrictions on population mobil-ity in the Western region of Ukraine and changes in the distribution of passenger flows between different modes (bus, rail, private transport, joint travel). In 2020, the volume of passenger traffic in the region decreased by an average of half compared to the previous year. The decline is sharper for rail passenger transport compared to the bus transport. For more developed railway networks, the impact of the pandemic on passenger traffic is more pronounced. Quarantine restrictions have also increased the share of own car travel. According to research, the distribution of intercity trips between modes is influenced by the age and sex of the traveler. During the pandemic, users of transport services who travel with children under the age of 14 choose private transport to travel more often than those who travel alone. The degree of influence of the above factors on the distribution of modes depends on the length of the trip. The application part of the work presents the results of modeling passenger flows of the studied region in the software environment PTV Visum. It was found that at the beginning of the quarantine restrictions the number of intercity trips decreases sharply. As the duration of restrictions increases, the rate of decline in mobility decreases. These data can be further taken into account when planning the work of transport enterprises and meeting the population`s demand for travel. The practi-cal application of the study results is that the identification of trends in the mobility of residents of the studying region depending on the impact of pandemic restrictions allows you to predict the mode and type of vehicles used. Based on these data, you can determine marketing strategies for the development of certain modes and directions of transporta-tion.

Semi-supervised Mode Classification of Inter-city Trips from Cellular Network Data

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Emission Impacts of Post-Pandemic Travel Behaviour in Intercity Corridors

COVID-19 pandemic impacted the behaviour of travellers. While worldwide, overall emissions decreased during the lockdown, shared mobility options may be perceived as less safe in a post-pandemic reality, leading to increased emissions due to expanded individual transportation. In particular, intercity trips entail numerous environmental impacts, such as emissions. The main objective of this paper is to assess how intercity corridors’ emissions vary when travel behaviour of the population changes following a pandemic. Based on a macroscopic modelling framework, the methodology consisted of three main phases: data collection, traffic modelling and emission modelling. Different scenarios related to the impact of the pandemic were developed, and their impacts were analysed using several key performance indicators related to CO2 NOx emissions and travel time. Findings suggest that reducing the average number of occupants per vehicle reduces emissions, which do not increase linearly with the number of vehicles. Compared with the baseline scenario (occupancy rate of 1.30), the most extreme scenario (occupancy rate of 1.00) may result in an increase in both CO2 and NOx emissions by approximately 30%. These results highlight the importance of making public transport and carpooling not only safe but also safe as perceived by users.

Impact of mobility on COVID-19 spread – A time series analysis

In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in the number of infected people. In addition, the results confirmed that super-spreading events had significant increases in the number of positive cases. We have also assessed the impact of major events and holidays throughout the study period and analyzed the impacts of mobility patterns in those situations. Our analysis shows that holidays without inter-city travel bans have been associated with a 27% increase in the number of weekly positive cases. As such, while holidays decrease transit usage, it can overall negatively affect spread control if proper control measures are not put in place. The result and discussions in this paper can help authorities understand the effects of different strategies and protocols with a pandemic control and choose the most beneficial ones.

A Multimodal Transport Model to Evaluate Transport Policies in the North of France

We developed a passenger transport model for the North of France and used it to discuss the impacts of some policies focusing on the limitations of polluting gas emissions and congestion. The model is calibrated for the North of France and includes both urban and intercity trips. Four transport modes are considered: walking, biking, public transport and private cars. To some extent, the combination of these modes is possible. The model is calibrated to match mode shares and the dynamic of congestion along a full day. The simulations are conducted within the MATSim framework. We evaluate the impacts, on traffic flows and polluting gas emissions, of two pricing reforms: free public transport and road pricing in city center of Lille (the main metropolitan area in the study region). Free public transport yields a significant modal shift towards public transport, resulting in a reduction in the usage of private cars. The road pricing scheme we have considered results in similar impacts but with limited magnitude. Overall, a significant reduction in congestion and emissions of pollutant gases can be obtained by applying convenient pricing reforms. Since we use an agent-based model, we are able to identify the specific location of the main impacts on the network.