International Journal of Intelligent Transportation Systems Research (2021) 19:429–440 https://doi.org/10.1007/s13177-021-00255-4 Smart Roads Geometric Design Criteria and Capacity Estimation Based on AV and CAV Emerging Technologies. A Case Study in the Trans-European Transport Network Marco Guerrieri1 Received: 20 January 2021 /Revised: 21 February 2021 /Accepted: 2 March 2021 / Published online: 19 April 2021 # The Author(s) 2021 Abstract Smart roads, AV and CAV are emerging technologies that represent the new paradigm of mobility. To support the public and private road operators better prepare themselves to implement these technologies in their respective existing or planned infra- structures, there is an urgent need to develop an integrated analysis framework to evaluate the impact of these novel systems on road capacity and safety in function of different market penetration levels of AVs and CAVs. The research focuses on novel smart road geometric design and review criteria based on the performance of AVs and CAVs. The case study of one of the first planned smart roads in Italy has been analysed. Keywords Smart roads . AVs . CAVs . Design criteria . Capacity estimation 1 Introduction In addition to the essential C-ITS technologies, one or more of the following systems can be implemented in smart road Smart roads represent the new paradigm of mobility and aim Environment (SRE) [1]: at being more sustainable, safe, innovative and inclusive, thus modifying the traditional relationship between vehicles, users – Lanes dedicated to AVs and CAVs travelling isolated or and infrastructure/environment. Smart roads are digitalized in platoons; infrastructures increasingly compatible with the new technol- – Internet of Things (IOT): sensors for monitoring traffic ogies employed in light and heavy vehicles, with specific re- flows, structures (bridges, viaducts, road safety barriers gard to the more recent Autonomous Vehicles (AVs) and etc.), weather and air pollutants; connected and automated vehicles (CAVs). CAVs can moni- – Ramp-metering systems; tor multiple vehicles ahead by the vehicle-to-vehicle (V2V) – Hard Shoulder Running (HSR) systems; communication. – Variable Speed limits (VSL); Basically smart roads adopt cooperative intelligent trans- – Green Islands (GIs): a multi-technological site (e.g. portation system technologies (C-ITS) in order to enable the one for each 20–30 km highway segment) for gen- communication and cooperation both among all the vehicles erating energy from renewable sources (i.e. photo- and among the latter and road facilities. voltaic cells, mini-wind turbines, etc). GIs allow for Lately, by means of the C-Roads Platform, many road op- a road or highway segment to be power-supplied, erators and technical authorities have joined together to har- thus lowering operating costs. Each Green Island monise the deployment actions of C-ITS in the European can be equipped with power recharge points, drone countries. The main objective is the deployment of interoper- areas for landing and take-off used to survey the able cross-border C-ITS services for road users, especially the traffic streams and deliver the first-aid kit like, for so-called “Day 1 - C-ITS service”. instance, portable defibrillators; – Electric priority lanes (lanes only for electric vehicles (EVs) [2] equipped with wireless recharge technologies); – * Charge vehicles system on specifically assigned lanes; Marco Guerrieri – [email protected] Piezoelectric devices to generate electrical energy: pie- zoelectric crystals are placed about 5 cm below the as- 1 DICAM, University of Trento, Trento, Italy phalt pavement surface. The piezoelectric crystals 430 Int. J. ITS Res. (2021) 19:429–440 Table 1 Summary of existing relevant studies (adapted from Study Focus and application Approach [7]) Shladover [9] CAV platooning capacity implications mathematical formulation Vander Werf et al. [10] CV impacts on traffic flow capacity Monte-Carlo simulation Tientrakool et al. [11] partially AV impact on highway capacity numerical simulation Fernandes et al. [12] CAV platooning capacity implications traffic simulator (SUMO) Shladover et al. [13] CV impact on highway traffic simulator (AIMSUN) Milanes et al. [14] CV impact on traffic flow stability Empirical study Arnaout et al. [15] V2V impact agent-based Friedrich [16] CAV impact on traffic flow condition mathematical formulation Hussain et al. [17] CAV freeway lane management mathematical formulation Talebpour et al. [18] CAV impact on shockwave formation integrated simulation framework Chen et al. [19] AV impact on operational capacity mathematical formulation Ghiasi et al. [20] CAV impact on highway capacity Markov-chain analysis Ye and Yamamoto [21] CAV impact on traffic flow condition numerical simulation Olia et al. [22] AV and CAV impact on highway capacity traffic simulator (Paramics) Abdulsattar et al. [7] CAV impact on highway capacity agent-based Guerrieri et al. [8] CAV impact on highway capacity mathematical formulation became slightly deformed when vehicles travel across the To support the public and private road operators better road and produce electrical current [3]; prepare themselves to implement C-ITS, AV, CAV technolo- – Programmable wireless digital traffic sign systems; gies and smart roads in their infrastructures network, there is – Static weighing to weigh-in-motion (WIM), virtual WIM an urgent need to develop an integrated analysis framework to and High speed weigh-in-motion (HS-WIM); evaluate the impact of these novel systems on road capacity – Smart intersections that allow reducing fatal collisions and safety [7]. and enhancing safety at road intersections. Using intelli- At present, neither technical standards nor guidelines on gent cameras, object recognition and V2X the connected geometric design criteria for such new digitalized infrastruc- vehicle can warn the driver of a pedestrian crossing, even tures are available. when they are not directly within line of sight [3]; In [8] it has been formalised some novel highway design – Smart street lights with sensors-added used for a variety criteria founded on the performance of AVs and CAVs. of purposes, including [3] (a) gunshots, terrorists and riots In addition, these criteria allow assessing whether an detection, (b) air quality monitoring, (c) EV charging existing highway could be used by CAVs safely. points, (d) traffic congestion monitoring, (e) people Instead, numerous studies have investigated the potential crowd monitoring, (f) public safety monitoring, (g) road- impact of CAVs on highway capacity. Generally, two differ- side parking monitoring, and (h) trash and littering ent types of approach are used [7]: (i) microscopic traffic monitoring; simulations; (ii) analytical models. Table 1 shows a summary – Road safety barriers equipped with an accidents moni- of some of the most relevant literature. All the research results toring system (AMS) able to detect the impacts against the showed an increase in highway lane capacity in the range barriers in real-time (e.g. NDBA safety barrier [4]). 180%–500% (Fig. 1) with correlated remarkable environmen- tal benefits [23, 24]. The exchange of several types of information (i.e. po- sition, speed, acceleration and highway alignment param- 600 500 500 eters) between the vehicles by V2V, V2I and V2X sys- 400 300 400 250 273 250 tems will enable the CAVs to anticipate the forthcoming 181 200 accident risk in surroundings traffic stream resulting in [%] accident-free and smooth driving under complex traffic 0 Maximum lane environment [5]. increase capacity 1991 2016 2021 Shladover Machine Learning (ML), Deep Learning (DL), Neural net- 2012 2012 al. 2011 Meissneral. et al. et Guerrieri works and in general Artificial Intelligence (AI) are highly Tientrakool et Brownell 2013 Brownell Fernandes et al. Olia et al. 2018 al. Olia et Shladover et al. used to increase the success rate of the emerging transporta- Fig. 1 Highway lane capacity increase due to smart road, AV and CAV tion technologies [6]. technologies in accordance with recent research Int. J. ITS Res. (2021) 19:429–440 431 CAVs are still at the theoretical and experimental phases of In this research the design and review criteria (formalised development. In the near future different market penetration in [4]) founded on the performance of emerging AV and CAV levels (MPLs) of CAVs are expected. In accordance with technologies are used for the design review of the existing MPLs, different penetration rates η of AVs and CAVs in the Italian A19 motorway that will be one of the first smart roads total highways traffic volume are plausible in the coming de- in Italy. In addition, the expected increase in motorway capac- cades as follows: ity is estimated. The methodological approach from this research and the – Market penetration level 1 (MPL1): only manually driven results of the analyzed case study may represent one of the vehicles (η =0); first tools to include the emerging CAV technologies in the – Market penetration level 2 (MPL2): mixed traffic flow decision processes concerning the novel digitalized- comprising of both manually driven vehicles and CAVs infrastructure planning and designing. (0 < η <1); The remainder of the article is structured as follows: as the – Market penetration level 3 (MPL3): only CAVs (η =1). first section introduced the main research objectives, Sections 2 and 3 present the proposed Design criteria and capacity Obviously, new highways will need to be designed specif- model for smart roads