03-highway.Rmd

# Highway infrastructure management {#highway}

## Unmanned aerial vehicles for infrastructure maintenance {#uav}

### Synonyms {-}
*Drones, remotely piloted vehicles, remotely piloted aircraft, uav*

### Definition {-}
Unmanned Aerial Vehicles (UAVs), commonly known as drones are promising technologies that can be used in inspection and data gathering for infrastructure maintenance and management purposes. These include, for example, detection of wear and tear, monitoring of the progress at a highway construction site or the analysis of traffic (Frederiksen et al., 2019). UAVs typically include a portable control station for the human operator and under current legislation their operation in urban areas is limited to flying within visual line of sight (VLOS). UAVs typically feature various sensors and recorders, including video, far and near infra-red, radar or laser-based range finders and specialized communication devices (Shaghlil & Khalafallah, 2018). Majority of them can transfer real-time data between the UAV and the control station. Moreover, some feature additional on-board data storage capabilities for enhanced data collection (Shaghlil & Khalafallah, 2018). The use of drones for infrastructure-related tasks provide not only savings with respect to time, labour and costs, but they also allow for reduction in risks when dangerous operations usually performed by human can be substituted with drones. Finally, the environmental impact is diminished when drones, which produce considerably less CO~2~, are used instead of currently employed helicopters. Nevertheless, the use of drones as a tool for inspecting infrastructure can also pose certain challenges with respect to current technology, legal framework, privacy concerns and social acceptance.

### Key stakeholders {-}

- **Affected**: Direct users of the roads and beneficiaries affected by the supply of transport services
- **Responsible**: Government agencies responsible for planning, executing, and financing of maintenance activities, citizens, contractors and subcontractors, private companies and manufacturers

### Current state of art in research {-}
Current research efforts and field trials-based studies are advocating the case of using UAVs for bridge inspection and monitoring. Previous study presented a proof of concept of utilising UAVs for bridge and high mast luminaires. Several experiments in controlled conditions were performed to test UAV response in relation to wind conditions. Moreover, image quality was examined in different flight scenarios, low light conditions, altitude and payload (Otero et al., 2015). Overall, the results are in favour of using drones for infrastructure inspections, not just in terms of saving human labour but also detecting the damages. The advantages of the drone use were also demonstrated in terms of reduced traffic control and decreased use of under bridge inspection vehicles (Zink and Lovelace, 2015). On the other hand, the lack of specific skills of the drone operators were found to hinder efficient use of drones for large-scale bridges (Wu et al., 2018). Further, some technological barriers also slow down the popularity of drones in infrastructure inspection, where an average flight time of the drone given its battery life is approximately 30 minutes. Therefore, current research aims at increasing the energy-efficiency by the use of path planning and algorithms to minimize energy utilization while maximizing coverage for traffic monitoring (Outay et al., 2020). 

### Current state of art in practice {-}
Current use of drones is heavily regulated by national and international governments worldwide where the most considerable restriction is the requirement for drones to remain under VLOS of the controller. Beyond, the regulatory bodies put forward various specification with respect to physical aspects of the drones such as weight or sensors, training requirement of the operators and drones’, data acquisition regulations and operation itself such as flight timeframe, altitude etc. (FAA, 2016; Outay at al., 2020). All of them, significantly restrict fast and wide application of drones in different areas. Therefore, the authorities attempt to provide regulations to tackle safety and privacy as well as noise concerns of the citizens. At the moment drones are used in oil and gas industry to conduct local surveys in off-shore facilities (Undertaking, 2016). Meanwhile in the transport sector, Danish company Dronops, after safety clearance, has been granted permission from Danish Road Authority to fly along a highway to monitor the traffic, where drone provides data from multiple sensors as well as video recordings. At the moment, the drone can only fly in good weather conditions and it is cable-linked to its power source located on the ground to allow for continuous day-long monitoring at 120 m above the ground. Importantly, the output data is used by Danish Road Authority and local council (Frederiksen et al., 2019).

### Relevant initiatives in Austria {-}

- [smartcity.wien](https://smartcity.wien.gv.at/site/en/smart-inspection/) 

### Impacts with respect to Sustainable Development Goals (SDGs) {-}

```{r table5, echo=FALSE, results='asis'}
cat('| Impact level| Indicator|Impact direction| Goal description and number|Source|
|:------------:|:-----------:|:------------:|:------------:|:------------:|
|Individual|Employees risk reduced|**+**|Health & Wellbeing (*3*)|Outay et al., 2020|
|Systemic|Road safety increased|          **+**|Health & Wellbeing (*3*)|Outay et al., 2020|
|Systemic|Emissions rate reduced|         **+**|Environmental sustainability (*7,12,13,15*)|Outay et al., 2020|
|Systemic|Job posts created|          **+**|Sustainable economic development (*8,11*)|Jenkins & Vasigh, 2013|
|Systemic|Faster road infrastructure innovation|          **+**|Innovation & Infrastructure (*9*)|Fan & Saadeghvaziri, 2019|')
```

### Technology and societal readiness level {-}

```{r table6, echo=FALSE, results='asis'}
cat('|TRL| SRL|
|:----:|:----:|
|3-4|5-7|')
```

### Open questions {-}
1.	What are the factors influencing social acceptability of drones?
2.	What actions from the policymakers need to be undertaken to minimalize cyber-attacks?
3.	What aspects need to be considered by the governments before the integration of more sensors to record other relevant data along with the integration of video data with other geospatial information?

### Further links {-}

-	[rolandberger](https://www.rolandberger.com/en/Insights/Publications/Drones-The-future-of-asset-inspection.html) 

### References {-}
- FAA News, 2016, Summary of Small Unmanned Aircraft Rule (Part 107), Federal Aviation Authority, Washington DC, 20591, Accessed on May 2020, https://www.faa.gov/uas/media/Part_107_Summary.pdf.
- Fan, J., & Saadeghvaziri, M. A. (2019). Applications of Drones in Infrastructures: Challenges and Opportunities. International Journal of Mechanical and Mechatronics Engineering, 13(10), 649-655.
- Frederiksen, M. H., Mouridsen, O. A. V., & Knudsen, M. P. (2019). Drones for inspection of infrastructure: Barriers, opportunities and successful uses.
- Jenkins, D., & Vasigh, B. (2013). The economic impact of unmanned aircraft systems integration in the United States. Association for Unmanned Vehicle Systems International (AUVSI).
- Otero, L.D., Gagliardo, N., Dalli, D., Huang, W.-H., Cosentino, P. (2015). Proof of concept for using unmanned aerial vehicles for high mast pole and bridge inspections (No. BDV28-977-02). Florida. Dept. of Transportation. Research Center.
- Outay, F., Mengash, H. A., & Adnan, M. (2020). Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges. Transportation research. Part A, Policy and practice, 141, 116–129. https://doi.org/10.1016/j.tra.2020.09.018
- Shaghlil, N., & Khalafallah, A. (2018). Automating highway infrastructure maintenance using unmanned aerial vehicles. In Construction Research Congress (2-4).
- Undertaking, S. J. (2016). European drones outlook study. Unlocking the Value for Europe.
- Wu, W., Qurishee, M. A., Owino, J., Fomunung, I., Onyango, M., Atolagbe, B. (2018). Coupling deep learning and UAV for infrastructure condition assessment automation. In: 2018 IEEE International Smart Cities Conference (ISC2). IEEE, pp. 1–7.
- Zink, J. and Lovelace, B., 2015. Unmanned aerial vehicle bridge inspection demonstration project. Research Project. Final Report, 40. Accessed in Nov 2020


## Electric charging stations {#charging_station}

### Synonyms {-}
*electric vehicle charging station, EV charging station, electric recharging point, charging point, electronic charging station (ECS), electric vehicle supply equipment (EVSE)*

### Definition {-}
Nowadays, the use of electric vehicles is continuously increasing, hence it is not surprising that both, governments and private companies have strong interest in expanding EV charging infrastructure to ensure uninterrupted travelling and stimulate consumer adoption. There are three main types of charging stations, depending on the power output (measured in kilowatts (kW)) and consequently charging speed. These are:

-	**Rapid**

They are the fastest way to charge an EV, therefore, this type is most frequently deployed near main routes and at highway service stations. They provide tethered cable and high power of direct (DC) or alternating current (AC). Vehicles can be charged in approximately 20 minutes up to 80%, but, on average it takes about an hour for a new EV. *Rapid DC* chargers use either [CHAdeMO](https://chademo.com/) or CCS charging standards. These are two most popular types at the moment. They provide power of 50 kW. A newer type are *Ultra Rapid DC* chargers that have power output of at least 100 kW. Further, *Rapid AC* chargers provide power of 43 kW and use the [Type 2](https://www.mobilityhouse.com/int_en/knowledge-center/charging-cable-and-plug-types#:~:text=Type%202%20plug,-The%20triple%2Dphase&text=In%20private%20spaces%2C%20charging%20power,with%20a%20type%202%20socket.) charging standard. Finally, a special network of chargers are *Tesla’s superchargers* which are custom made for Tesla vehicles. Nonetheless, many Tesla drivers use adapters that enable them to use widely-available generic chargers (Lilly, 2020). 

-	**Fast**

Fast charging units typically provide AC charging with power output of 7kW or 22kW. Nevertheless, it is also possible to find stations that use 25 kW DC chargers with CHAdeMO or CCS charging standards. The charging times range between 1-6 hours depending on the battery installed in the vehicle. Fast chargers are usually located at the car parks, supermarkets, or leisure centres, where cars are likely be parked at for an hour or more. Fast charging units can be both tethered and untethered (Lilly, 2020).

-	**Slow**

The power output of slow chargers varies between 2.3 kW to 6 kW and takes from 6 to 12 hours. Most slow charging units are untethered, meaning that the driver needs to supply his own cable to connect the EV with the charge point. They are usually used to charge at home overnight but also at workplace and at public points. Because of the longer charging times over fast units, slow public charge points are less common and may be older devices.

Finally, it is important to mention that the development of public EV charging stations is an important factor that influences the adoption of electric vehicles, nonetheless, the high investment costs (including land acquisition, installation, operation and maintenance) and low profitability (that heavily relies on their use) are considered key barriers to the faster development of charging stations (Lilly, 2020). 


### Key stakeholders {-}

- **Affected**: EV drivers
- **Responsible**: National enterprises and governments, international oil and gas companies, automotive companies, start-ups in the green energy sector, transport authorities

### Current state of art in research {-}
Charging stations for electric vehicles are now a well-established technology in itself. Therefore, current research focuses on the exploration of their potential when combined with other new technologies. For example, Lokhandwala & Cai (2020) use New York City taxis as a case study to considers possible interactions between charging infrastructure for electric vehicles, ride sharing (RS) and autonomous vehicles (AV). For this purpose, they compare the optimal charging infrastructure expansion plans for three cases: non-AV-RS scenario (present), AV-RS scenario (future) and a mixed case. The study simulation shows that for the AV-RS case the use of charging stations is more spread throughout the day as compared to non-AV-RS where the demand and queuing are the highest in the mornings. In terms of the service levels, it is found that in the non-AV-RS scenario the service level remained unchanged as a result of the adoption of EV while in the AV-RS case, the level decreased by 2%. Finally, the study shows that EV taxis were predicted to achieve reduction in CO~2~  emission in both scenario types. <br/>

Additionally, a large strand of research focuses on optimising the locations of charging station for various vehicle types such as taxis (Zhang et al, 2019), buses (Uslu & Kaya, 2021; Wu et al., 2021; He et al., 2019) and private EVs (Pan et al., 2020) considering different factors, for instance, social acceptance, EV adoption rate, demand fluctuations, trip length (Anjos et al., 2020) and road infrastructure types including highways (Napoli et al., 2020) and urban network (Ji et al., 2020). <br/>

Finally, an important part of the research looks at the role of charging station infrastructure in the adoption and use of electric vehicle which are considered a solution to reduce green-house gas emissions and air pollution (Philipsen et al., 2019; Bonges et al., 2016). For instance, the study by Melliger et al. (2018) explored how the expansion of the charging station network can counteract phenomenon of so-called ‘*range anxiety*’ in EV buyers related to the capacity of EV battery and availability of charging point along the routes (Melliger et al., 2018). 
 

### Current state of art in practice {-}
According to the Global EV outlook 2020 (IEA, 2020), the number of global charging stations reached 7.3 million chargers in 2019. Of this number, 6.5 million are slow and normal chargers which are installed at home, residential, and office buildings while the rest are publicly accessible chargers (Thananusak et al., 2021). <br/>

Denser network of charging stations has been showed to be an important factor in counteracting *range anxiety* in EV users (Philipsen et al., 2019). Therefore, it is not surprising that policies of many countries in Europe and beyond set the goals for the construction of EV charging stations to encourage their uptake. For example, France introduced so-called [Law on Energy Transition for Green Growth (LTECV)](https://www.iea.org/policies/8737-law-on-energy-transition-for-green-growth-ltecv) which aims at building 7 million charging station by 2030 (Thananusak et al., 2021). Similarly, in Switzerland the Federal Road Office (FEDRO) recommended installation of fast-charging stations at all main highway service areas (Melliger et al., 2018). In terms of EU-wide legislation, *[Directive 2014/94/EU](https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32014L0094&from=en) on the deployment of alternative fuels recharging and refuelling infrastructure* (known as Alternative Fuels Infrastructure Directive (AFI)) requires the EU member states to provide an appropriate number of publicly accessible charging points. <br/><br/>
In Austrian context, the study by Baresch & Moser (2019) showed that 88% of EV users charges their cars at home and only 1.5% - 1.7% uses public stations. This significantly influences the profitability of public stations. To alleviate this problem, governments can use demand-push or technology-push policies. The demand-push solutions are based on extending the markets for innovations to induce the demand and increase the profits from charging station business. These include, for example, consumer awareness building, rebates and tax credits. On the other hand, the technology-push solutions are aimed at decreasing the cost of innovative solution, through funding and subsidies for R&D in private sector (Thananusak et al., 2021).<br/><br/>
Interestingly, between 2007 and 2013 first transnational electric mobility project took place between Vienna and Bratislava. It aimed at demonstrating the operationability of a cross-border initiative. For this purpose, a number of charging stations have been built in public and semi-public places to allow users of electric vehicles to charge their cars on either side of the border. The project showed the user-friendliness of electric vehicles in daily traffic and the trans-border availability of services, no matter what the country of residence of the e-mobility customer (Verbund, 2011).

### Relevant initiatives in Austria {-}
- [ieahev.org](http://www.ieahev.org/by-country/austria-charging-infrastructure/)
- [ev-charging.com](https://ev-charging.com/at/en/elektrotankstellen)
- [smatrics.com](https://smatrics.com/en/charging-network)
- [chargemap.com](https://chargemap.com/cities/wien-AT)


### Impacts with respect to Sustainable Development Goals (SDGs) {-}

```{r table84, echo=FALSE, results='asis'}
cat('| Impact level| Indicator|Impact direction| Goal description and number|Source|
|:------------:|:-----------:|:------------:|:------------:|:------------:|
|Systemic|Contribution to emissions reduction (via EVs)|**+**|Environmental sustainability (*7,12,13,15*)|Thananusak et al., 2021|
|Systemic|Increased public funding on charging stations but high entry barriers for investors|**~**|Innovation & Infrastructure (*9*)|Thananusak et al., 2021|
|Systemic|Encourages collaboration between different stakeholders (e.g. Industry and Governments) and countries|**+**|Partnership & collaborations  (*17*)|Thananusak et al., 2021|')
```

### Technology and societal readiness level {-}

```{r table85, echo=FALSE, results='asis'}
cat('|TRL| SRL|
|:----:|:----:|
|8-9|6-8|')
```

### Open questions {-}
1. What are demand-pull and technology-push policies to increase investment in EV charging stations? What is their relative effectiveness?
2. What share of car trips can be successfully covered with the currently available (2021) BEVs and the current charging infrastructure?
3. What are the needs of Austrian car users with respect to the driving range?

### Further links {-}

-	[plugshare.com](https://www.plugshare.com/en) 
-	[dekra-product-safety.com](https://www.dekra-product-safety.com/en/ev-charging-station-technology) 

### References {-}
- Anjos, M. F., Gendron, B., & Joyce-Moniz, M. (2020). Increasing electric vehicle adoption through the optimal deployment of fast-charging stations for local and long-distance travel. European Journal of Operational Research, 285(1), 263-278.
- Bonges III, H. A., & Lusk, A. C. (2016). Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation. Transportation Research Part A: Policy and Practice, 83, 63-73.
- He, Y., Song, Z., & Liu, Z. (2019). Fast-charging station deployment for battery electric bus systems considering electricity demand charges. Sustainable Cities and Society, 48, 101530.
- IEA. (2020). Global EV Outlook 2020; IEA: Paris, France. p. 276.
- Ji, D., Lv, M., Yang, J., & Yi, W. (2020). Optimizing the Locations and Sizes of Solar Assisted Electric Vehicle Charging Stations in an Urban Area. IEEE Access, 8, 112772-112782.
- Lilly, C., (2020). EV Charging connectors - Electric car charging speeds.  Zap-Map. Available at: <https://www.zap-map.com/charge-points/connectors-speeds/#:~:text=There%20are%20three%20main%20types,measured%20in%20kilowatts%20(kW).> [Accessed: 26 March 2021].
- Lokhandwala, M., & Cai, H. (2020). Siting charging stations for electric vehicle adoption in shared autonomous fleets. Transportation Research Part D: Transport and Environment, 80, 102231.
- Melliger, M. A., van Vliet, O. P., & Liimatainen, H. (2018). Anxiety vs reality–Sufficiency of battery electric vehicle range in Switzerland and Finland. Transportation Research Part D: Transport and Environment, 65, 101-115.
- Napoli, G., Polimeni, A., Micari, S., Andaloro, L., & Antonucci, V. (2020). Optimal allocation of electric vehicle charging stations in a highway network: Part 1. Methodology and test application. Journal of Energy Storage, 27, 101102.
- Pan, L., Yao, E., Yang, Y., & Zhang, R. (2020). A location model for electric vehicle (EV) public charging stations based on drivers’ existing activities. Sustainable Cities and Society, 59, 102192.
- Philipsen, R., Brell, T., Biermann, H., & Ziefle, M. (2019). Under Pressure—Users’ Perception of Range Stress in the Context of Charging and Traditional Refueling. World Electric Vehicle Journal, 10(3), 50.
- Thananusak, T., Punnakitikashem, P., Tanthasith, S., & Kongarchapatara, B. (2021). The Development of Electric Vehicle Charging Stations in Thailand: Policies, Players, and Key Issues (2015–2020). World Electric Vehicle Journal, 12(1), 2.
- Uslu, T., & Kaya, O. (2021). Location and capacity decisions for electric bus charging stations considering waiting times. Transportation Research Part D: Transport and Environment, 90, 102645.
- Verbund.com (2011). Europe Premiere: Unlimited Electric Mobility - VERBUND.  Verbund.com. Available at: <https://www.verbund.com/en-de/about-verbund/news-press/press-releases/2011/03/04/unlimited-electric-mobility> [Accessed: 26 March 2021].
- Wu, X., Feng, Q., Bai, C., Lai, C. S., Jia, Y., & Lai, L. L. (2021). A novel fast-charging stations locational planning model for electric bus transit system. Energy, 120106.
- Zhang, S., Wang, H., Zhang, Y. F., & Li, Y. Z. (2019). A novel two-stage location model of charging station considering dynamic distribution of electric taxis. Sustainable Cities and Society, 51, 101752.