1. Introduction
In Poland, where a private car is still perceived as something of a status symbol by a substantial number of citizens [
1], and especially in large Polish cities, one can easily observe how complex it is to implement electromobility as yet another transport solution—especially when some people would even be willing to give up the idea of car ownership if the alternatives of city transport met their specific transport needs [
2].
Before the idea of electromobility was recognised at governmental level and then delegated to lower grades of public administration, major Polish cities were a testing ground for the first such initiatives, which were often of an episodic or experimental nature. These included decisions to purchase electric buses and to launch EV car-sharing programmes in order to improve air quality, reduce noise levels, and raise standards of passenger transport. Furthermore, these decisions were also partially triggered by regional green marketing.
Although a national policy on the main electromobility [
3] objectives has been established, as well as the tools for its implementation and the indicators by which to measure the efficiency of the whole process, the burden of implementation still lies with local governments. This particularly concerns those authorities of regions where transport systems play a crucial role, i.e., large cities. This has resulted in local governments having to develop and effectively implement their own mobility strategies, which include an objective to increase the importance and presence of EVs. Implementation of the objectives of mass transit—which in large cities is customarily controlled by local authorities—is primarily a financial challenge, since it usually means replacing the existing fossil-fuel-powered bus fleet with vehicles that have alternative and more eco-friendly powertrains. The ongoing shift to electromobility in public transport (here: buses) also involves the need to adapt to the challenges that the applied technology imposes (e.g., extra time allocated to charging vehicles), yet all of these procedures are mainly coordinated and controlled by local governments [
4]. Another issue is the percentage of vehicle-kilometres and passenger-kilometres covered by eco-friendly vehicles against the total value of these parameters for a given public transport system.
The implementation of the objective of increasing the share of privately owned EVs requires radical thinking. Even though large cities have successfully introduced electric rental alternatives to public transport (e.g., scooters, bikes, mopeds, etc.), encouraging privately owned electric car transport still remains quite a challenge. Admittedly, there are some shared vehicle fleets, but EVs usually constitute just a tiny fraction of these.
Currently, city authorities are facing a most difficult task related to the development of public transport—changing transport behaviour. In the case of private vehicle use, factors that make it even more formidable are the financial barrier (the high prices of EVs), the psychological barrier (distrust towards the technology behind fully electric powertrains due to concerns about limited charging possibilities in emergency situations and the relatively low driving range), and the infrastructural barrier (e.g., an insufficient network of fast charging stations).
However, it should be stressed that the technology related to the process of charging vehicles and the durability of their batteries is constantly and dynamically developing [
5]. The solutions being developed are designed to reduce the time required to charge the battery while minimising the impact on battery longevity [
6,
7]. On the other hand, efforts to develop the batteries themselves concentrate on increasing their capacity, prolonging the period of time for which they can provide energy of appropriate parameters, and extending their useful life [
8,
9,
10,
11]. These actions are intended to fulfil both the objective of increasing the effectiveness/convenience of their use for final consumers (in this case, drivers of passenger cars) and the objective of limiting their negative impact on the environment—for instance, during their production, or at the end of their useful life [
12,
13]. With the emergence of greater awareness of sustainable and clean energy, batteries with lithium-ion technology, among others, have found widespread use in energy storage systems, including in electric vehicles. However, in order for this technology to meet the adopted performance criteria for batteries, it is necessary to manage the production chain precisely (which, as a result, is also less harmful to the environment). Electric and hybrid vehicles are associated with green technologies and the reduction of greenhouse gas emissions. They nevertheless contribute to increased environmental degradation through excessive demand for energy sources (especially in countries with limited access to renewable energy sources). The chemical and electronic components of car batteries, and their handling when they have lost their service value, involve continuous investment in the latest recycling technology, since it is necessary to limit the spread of electronic waste into the environment. Currently, many types of battery technology can be used in electric vehicles—including, for instance, lead–acid (Pb–acid), nickel–cadmium (Ni–Cd), nickel–metal hydride (Ni–MH), lithium-ion (Li-ion), lithium-ion polymer, and sodium–nickel chloride (Na–NiCl). Although individual solutions have different performance characteristics, none of them remain indifferent to nature. From this perspective, the development of biological systems for energy production in the context of bio-batteries seems to be an interesting alternative [
5]. It can thus be assumed that there will be a steady decrease in the role of technological and infrastructural barriers to the widespread use of electric and hybrid vehicles as a means of private transport.
In order to remove these barriers, local authorities are striving to overcome these “blank spots” on the map of charging stations, which pose a problem due to land ownership issues, the nature of land use in a given location, traffic management, and connection to the city’s often overloaded power grid. Furthermore, local governments may also impact residents’ transport behaviour using so-called ”soft” incentives or restrictions—the purpose of which is to privilege EVs when it comes to driving and parking within the urban area with regard to time, costs, etc. Such incentives may include free parking and unrestricted access to bus lanes. These are often implemented simultaneously with deterrents aimed at those vehicles considered exceptionally harmful to the environment. Alas, these actions are not undertaken at a sufficient scale, since there are legal, administrative, and political barriers, along with no public pressure due to lack of awareness. Moreover, when it comes to the stimulative role of local governments, it must be stated that they should aim to stay ahead of the curve in introducing new trends, instead of waiting passively for the price erosion of EV technology that may, with time, stem from further development and widespread popularisation of electromobility. The most vital initiatives are those that could either optimise or limit the deployment of infrastructure to critical spots, e.g., areas where a shortage of charging stations drastically restricts the usability of EVs. Importantly, local authorities must not fail to look for possible synergy between the development of charging infrastructure for cars and its own public transport [
14].
The main purpose of this article was to determine the impact on the equilibrium of the local transport system from privileging EVs by permitting them to use bus lanes. The study is based on microsimulation road traffic modelling for a designated research area that is part of the transport system impacted by an important traffic generator (a shopping centre) within the city of Łódź in Poland. The application of variables that describe the equilibrium of the transport system during afternoon rush hours makes it possible to determine the impact of EV traffic on the efficiency of the whole system. The simulation also allows the authors to determine the percentage of EVs in the total volume of vehicles at which traffic conditions deteriorate for all or individual groups of road users (internal combustion and hybrid cars, EVs, and public transport). The study can therefore be said to provide wide diagnostic material that facilitates appropriate policy decisions regarding the privileging of EVs that would be more flexible in terms of their changeability in time and space, and would indicate the percentage of EVs for which the incentive may begin to return counterproductive effects, i.e., it may result in the sui generis disruption of other road users and limit the efficiency of public transport—undoubtedly the most fundamental keystone of any policy of sustainable urban mobility. In contrast to numerous studies on the functional aspects of private electric car transport [
15,
16,
17,
18,
19]—which mainly focus on the distribution of charging points and their adaptation to the functional and spatial structure of the city or the transport needs of the population—our analysis focuses on the role that the growing number of electric vehicles will play in the local transport system. Assuming the aforementioned increase in the independence of vehicles from charging points in urban spaces (for instance, as a result of the increase in battery productivity), it seems justified to carry out research on the impact of electric vehicle traffic with special rights on the transport balance. The results of this type of research can be used to validate the process of implementing the assumptions of the sustainable urban mobility strategy [
20]; its most important element is reducing the environmental pressure resulting from the obligatory flows in the city space [
21]. The introduction of electrical solutions to power individual transport vehicles is necessary in this respect [
22,
23,
24]. Measurements of the vehicle traffic structure in the city—taking into account the division into electric vehicles and vehicles with other engines—are very important. The results of such observations provide data on the real share of EVs in traffic flows, and whether it affects the transport balance of the entire system.
4. Materials, Methods, and Limitations
4.1. Materials and Methods
This study is based on two important sources of data: The first is information on infrastructure and traffic management; in order to develop a microsimulation model, the authors conducted a field inventory (of land use, etc.) and a review of the relevant technical documentation obtained from the local roads authority. As a result, it was possible to accurately reflect the transport infrastructure (its location and dimensions, i.e., the width of carriageways and lanes) and the applied principles of traffic management (right of way, traffic light cycles, etc.) (
Figure 7). The second type of data contained information on recorded loads on the road network and traffic volumes generated by the shopping centre in question. Times and frequency of bus services were obtained from the local public transport operator (MPK-Łódź), while the values of loads on intersections were the results of empirical measurements taken from the induction loops (part of the ITS) that are installed on each lane within every single intersection that was analysed in this study (
Figure 6) [
88]. The data from the loops are from 16 March 2018 (Friday), taken between 4 p.m. and 5 p.m. in 15-min intervals. The ITS dataset was expanded with manual measurements taken for the only large traffic generator that has entrances and exits onto the analysed section of the network (the shopping centre). The measurements were conducted in 15-min intervals (for the purposes of the study, the authors used results from the period between 4 p.m. and 5 p.m. on Friday) (
Figure 7).
Two generators were added for each entrance to the analysed section of the network and attributed with traffic volumes returned by the detectors—and in the case of the exits from the shopping centre, also with data obtained via manual measurements. Next, maps of the traffic structure were developed and calibrated using data from the detectors located at access points (i.e., entrances to and exits from all intersections) within the analysed network, and on the first intersection outside the analysed network. The aforementioned infrastructure (the model of the applied network) and maps of the traffic structure were later used to develop a microsimulation model of traffic, built with PTV Vissim software.
The authors utilised the Wiedemann-74 car-following model for the applied traffic structure, which was divided into bus, car, and HGV. For the first group, the model determined the time when a given bus appeared within the analysed part of the network, based on the timetable details provided. During the field inventory, it was established that the percentage of HGVs in the total number of vehicles (excluding buses) amounted to 2%. The remaining traffic monitored by the induction loops was car traffic. On the basis of these premises, a 10-min simulation was conducted and later used as a base variant for comparison with subsequently simulated scenarios. Each successive simulation was performed on the basis of identical boundary and baseline conditions, with just one difference. The percentage of cars was recalculated afresh following the addition of a new type of vehicle—BEVs. The growth in the share of BEVs depended on the applied scenario (
Table 5), with a proportional decrease in the percentage of cars.
In scenario 1—which did not differentiate between various types of cars—the authors applied a base variant in which BEVs were not granted the privilege of using bus lanes. However, each subsequent simulation took this incentive into account. In scenario II, the share of BEVs in the traffic in the analysed section of the network reflected the actual percentage of BEVs in the total number of cars on Łódź roads (approx. 0.8 ‰). For the applied time horizon and loads recorded on the network, the model did not show any BEV traffic. In consequence, the results of the simulations in scenarios I and II were identical. Each simulation was observed in the resolution of 0.1 s.
The following average measures were applied for the comparative analysis of scenarios: difference between optimal driving time, duration of the state ”in queue” of the vehicle, and frequency of changes to the state ”in queue”. Additionally, dynamic analyses of the aforementioned measures were conducted for public transport. Deceleration to 5 km/h was nominated as the beginning of the queue, while its end was assumed once this speed was re-exceeded.
4.2. Limitations
The conducted analysis is associated with a certain group of limitations. At the beginning, it should be noted that due to the relatively high redundancy of the analysed network and its bus lanes (especially when the bus service was infrequent), the presence of privileged EVs hardly impacted driving times. The analysis presented in this paper must not be interpreted universally, since microscale simulations can only be applied for the area in which they were conducted—or other areas of very similar conditions and infrastructure. For the area researched in this study, the authors assumed that reserving bus lanes only for rather infrequent mass transit is not justified when the high redundancy of the network is taken into account. In such locations, granting BEVs access to bus lanes has no significant impact on bus traffic. The impact on the remaining urban traffic there is initially only negative, and once a certain threshold of BEV share (over 10% of all cars) is crossed, it begins to have a more positive influence, which primarily stems from the peculiar nature of the local transport infrastructure in the researched area. In addition, it should be noted that since it focuses on short journeys to a shopping centre, the study presented in this paper does not take into account the issue of the charging infrastructure, as this factor is of no significance here, which is another shortcoming of the analysis carried out.
5. Results and Discussion
According to Daina et al. [
84] and their classification of research on traffic modelling and electromobility, the study presented herein is a short-term analysis to evaluate, assess, and determine whether the load related to EVs may generate congestion in the distribution network (here: the network of mass transit).
In our simulation, a total of 496 vehicles appeared on the network within 10 min. The analysis in each simulation variant did not show any significant changes in the average value of delay when compared to the theoretical travel time (
Figure 8).
When the base variant was compared with other scenarios, the greatest changes of the analysed parameters were observed for BEVs and other cars (
Table 6). However, interpretation of values up to 15% of BEVs’ traffic share is impossible due to the potentially anecdotal nature of the analysed phenomena. A growth in BEVs’ share within the total volume of cars improves road conditions for all vehicles, which generally stems from an increase in the network capacity, resulting from a rise in the available space (i.e., use of the bus lane by an increasing number of privileged vehicles). The presence of privileged BEVs in the first simulation scenarios (up to 2% BEVs within the total number of cars) actually increased driving time on the remaining lanes for other car users, despite the fact that BEVs could use the bus lane and the road space increased. This stemmed from the fact that each bus lane near intersections is also a lane for turning right, and when BEVs used it to go straight across the intersection the lane was blocked for those road users who would otherwise be able to turn right as long as the traffic lights allowed them to. While privileging BEVs for the analysed section of the transport network when their percentage in the total number of cars is low leads to a slight deterioration of traffic conditions, a further increase in the percentage of BEVs (up to 20%) improves driving conditions for other road users. However, for high percentages of BEVs, the interpretation of results for other cars becomes impossible (the anecdotal criterion). The studies conducted by Zhang et al. [
96] show that access to bus lanes may have a negative impact on individual consumers, which could be caused by their concerns regarding dense traffic on public lanes for buses resulting from a growing number of BEVs on the road (which is unfavourable for EV users, as the privilege of using bus lanes then ceases to be effective). Our study confirms that a growth in the number of BEVs translates into worse driving conditions for their users. An additional issue from privileging EV drivers may be the fact that they are not familiar with the rules of traffic management that are specifically dedicated to mass transit (e.g., different-looking traffic signals). However, this problem does not affect the analysed case, as city buses use the same traffic lights as private car users
Table 6 presents the analytical scale for which it is impossible to perform an unambiguous statistical assessment of the impact that privileging BEVs has on bus traffic. Therefore, attempts to determine the phenomenon should be made through analysis (of high temporal resolution) that focuses on individual bus journeys, which would enable a more precise and clear-cut determination of the causes behind changes to the base scenario. A graphic illustration of said analyses is presented in
Figure 8. For almost all bus journeys, a rise in the percentage of BEVs resulted in a delay for the state “in queue” at the beginning of their journey through the analysed section of the network (BEVs that intend to go straight across the intersection block other vehicles wishing to turn right, thereby expanding the queue on the bus lane). During the subsequent stages through the researched area, the queueing times were practically unchanged (both in terms of the temporal distribution of queues and the time spent in a queue). In
Table 6, the changes in time that are noticeable were caused by one case (shown in
Figure 9), which is the effect of a disparity between differences in timetabled bus frequency on the bus lane and the cycle length of the traffic signal. These small changes are thought-provoking—after all, one would expect that limited space on bus lanes due to BEV privileging would worsen traffic conditions for buses. However, this effect was not observed in our study, which can be explained by the fact that bus lanes are usually separated from other lanes by a long stretch of solid line (indicating no crossing it), which makes them less attractive for BEV drivers intending to turn left ahead or even join the bus lane when the BEV is driving along a regular lane. Furthermore, the vast majority of bus lanes can only be joined from the intersection that precedes them. These issues are stressed by Sendek-Matysiak and Łosiewicz [
97], who argue that bus lanes are difficult for BEVs to enter and exit in many locations due to traffic restrictions or congestion on the remaining lanes. Moreover, the same authors also state that the problems with bus lanes and BEVs may be due to the somewhat confusing road markings that often discourage road users.
6. Conclusions
The purpose of this article was to determine the impact on the equilibrium of the local transport system from privileging EVs by permitting them to use bus lanes. Creating incentives to promote the purchase and use of a BEV is crucial in the context of creating transport systems suited to the principles of sustained urban mobility. However, incentives must be tailored to the given city and its residents. For instance, privileging BEVs with access to bus lanes does result in a rise in their percentage, but only to a limited extent, and the situation changes once a certain threshold of BEVs in the total number of cars is exceeded; this is because an increase in BEVs may make the privilege of using bus lanes less attractive for their users, as also shown by the results of our simulations. Such analyses are important not only from the methodological point of view, but also from the applicational perspective—by adopting the appropriate incentive, the city involved may encourage private users to buy a BEV.
Simulations that we have carried out can contribute to reliable uncertainty management, because uncertainty quantification could permit users to get more useful information for privileging EVs. Construction and analysis of simulation-based traffic models allows us to determine how likely certain outcomes are if certain aspects of the city transport system are not known in detail. One example of this could be forecasting delays in the performance of transport tasks by public transport vehicles with full knowledge of their timetables when bus lanes can be used by electric vehicles. The appearance of a private transport vehicle before the bus rather than immediately behind it may result in a situation where the public transport vehicle has to wait for the next phase of the traffic lights in order to be able to move, which may cause delays of up to several minutes at subsequent stops. Through a series of simulations we can obtain a group of different results that are only statistically predictable. This makes it possible to estimate the scale of possible time disruptions and, if necessary, to introduce the necessary organisational solutions (e.g., removal of an unnecessary bus lane).
Our analysis and similar studies provide diagnostic material that facilitates better policymaking when privileging EVs, and improves flexibility in terms of their changeability in time and space. They also show the threshold percentage of EVs for which privileging may return counterproductive results not only for EVs themselves, but also for other road users—including the efficiency of public transport, the most fundamental keystone of any policy of sustainable urban mobility.
The analyses carried out have produced interesting results with regard to both the research problem and several other aspects concerning the functioning of the local transport system. This justifies the need for further experiments and analyses, taking into account other research polygons (e.g., different traffic generators, different traffic organisation) and other equilibrium levels of the transport system (e.g., a different traffic peak period). This would allow for a broader and more accurate characterisation of the analysed phenomena, taking into account a group of case studies corresponding to the diversity of the urban transport system environment. Treating this study as initiating a series of further analyses, it should be pointed out that the obtained preliminary results strongly justify further in-depth research.
However, further studies could include the aspect of charging stations and their distribution, because the decision to purchase a BEV (and, thus, a rise in the share of electric cars among all vehicles on the urban network) is strongly correlated with the accessibility of charging points. This knowledge could also be utilised to assess the costs and benefits of building a network of charging stations.
Electric vehicles are the future for the world. Increasing their quantity will have a positive impact on the environment—which is confirmed by numerous studies [
98,
99]—but will also allow us to meet the requirements of sustainable development. However, one should not forget about the impact of this type of vehicle on the energy sector—including the costs of electricity, or the methods of its production; this is confirmed by the results of research conducted by Hassoun and Al-Sahili [
100]. In addition, research related to the charging infrastructure—including the impact of the EV charging station loads on the electricity distribution network—cannot be neglected, as noted by Deb et al. [
17]. In our article, we, in turn, attempted to evaluate the incentives and privileges introduced for users of this type of vehicle for urban transport systems, because this is an extremely important issue, and still very rarely discussed. The results of these studies are important for introducing recommendations for transport policies and documents related to sustainable development, not only at the local but also at the global level. This is also confirmed by the growing interest in this subject on the part of scientists, which translates into a greater number of publications in the field of EVs.